{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6d0f7da8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60e552d2",
   "metadata": {},
   "source": [
    "1.读取数据，查看前五行（查看除表头外的前5行）。查看数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eef1534d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./WA_Fn-UseC_-Telco-Customer-Churn.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a8214a64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()   #查看数据类型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9ffb42b",
   "metadata": {},
   "source": [
    "2.查看数据的最大值、最小值、平均数、四分位数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e47503c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.162147</td>\n",
       "      <td>32.371149</td>\n",
       "      <td>64.761692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.368612</td>\n",
       "      <td>24.559481</td>\n",
       "      <td>30.090047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>89.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>118.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeniorCitizen       tenure  MonthlyCharges\n",
       "count    7043.000000  7043.000000     7043.000000\n",
       "mean        0.162147    32.371149       64.761692\n",
       "std         0.368612    24.559481       30.090047\n",
       "min         0.000000     0.000000       18.250000\n",
       "25%         0.000000     9.000000       35.500000\n",
       "50%         0.000000    29.000000       70.350000\n",
       "75%         0.000000    55.000000       89.850000\n",
       "max         1.000000    72.000000      118.750000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()   # 查看详细信息，只会查看都是数值类型的列，因为只有数值类型的列才会有平均值那些"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5f2e6706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7043</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6531</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>3555</td>\n",
       "      <td>3641</td>\n",
       "      <td>4933</td>\n",
       "      <td>6361</td>\n",
       "      <td>3390</td>\n",
       "      <td>3096</td>\n",
       "      <td>3498</td>\n",
       "      <td>3088</td>\n",
       "      <td>3095</td>\n",
       "      <td>3473</td>\n",
       "      <td>2810</td>\n",
       "      <td>2785</td>\n",
       "      <td>3875</td>\n",
       "      <td>4171</td>\n",
       "      <td>2365</td>\n",
       "      <td>11</td>\n",
       "      <td>5174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        customerID gender Partner Dependents PhoneService MultipleLines  \\\n",
       "count         7043   7043    7043       7043         7043          7043   \n",
       "unique        7043      2       2          2            2             3   \n",
       "top     7590-VHVEG   Male      No         No          Yes            No   \n",
       "freq             1   3555    3641       4933         6361          3390   \n",
       "\n",
       "       InternetService OnlineSecurity OnlineBackup DeviceProtection  \\\n",
       "count             7043           7043         7043             7043   \n",
       "unique               3              3            3                3   \n",
       "top        Fiber optic             No           No               No   \n",
       "freq              3096           3498         3088             3095   \n",
       "\n",
       "       TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "count         7043        7043            7043            7043   \n",
       "unique           3           3               3               3   \n",
       "top             No          No              No  Month-to-month   \n",
       "freq          3473        2810            2785            3875   \n",
       "\n",
       "       PaperlessBilling     PaymentMethod TotalCharges Churn  \n",
       "count              7043              7043         7043  7043  \n",
       "unique                2                 4         6531     2  \n",
       "top                 Yes  Electronic check                 No  \n",
       "freq               4171              2365           11  5174  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='O')\n",
    "# include='O'中的O不是零，而是大写字母O\n",
    "# 其中答应出来的信息中索引代表的含义：\n",
    "# count:代表某个列一共有多少行数据 \n",
    "# unique：代表不重复的值有多少个，这个非常关键，可以通过这个信息查看这个列有几个值，来断定这列要不要做特征编码，来把object类型转为数值型\n",
    "# 而同时也可以判定我们到底是做独热编码还是label-encoding编码，如果列的值只有两个，那么做label-encoding编码，也就是这个列属于二元属性"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9e9a665",
   "metadata": {},
   "source": [
    "3.查看数据是否存在缺失值，并计算缺失值占比，将缺失值占比超过20%的特征进行删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "04337139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID          0.0\n",
       "gender              0.0\n",
       "SeniorCitizen       0.0\n",
       "Partner             0.0\n",
       "Dependents          0.0\n",
       "tenure              0.0\n",
       "PhoneService        0.0\n",
       "MultipleLines       0.0\n",
       "InternetService     0.0\n",
       "OnlineSecurity      0.0\n",
       "OnlineBackup        0.0\n",
       "DeviceProtection    0.0\n",
       "TechSupport         0.0\n",
       "StreamingTV         0.0\n",
       "StreamingMovies     0.0\n",
       "Contract            0.0\n",
       "PaperlessBilling    0.0\n",
       "PaymentMethod       0.0\n",
       "MonthlyCharges      0.0\n",
       "TotalCharges        0.0\n",
       "Churn               0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()/df.shape[0] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "72f461e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>6840-RESVB</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>84.80</td>\n",
       "      <td>1990.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>2234-XADUH</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>103.20</td>\n",
       "      <td>7362.9</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>4801-JZAZL</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.60</td>\n",
       "      <td>346.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>8361-LTMKD</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>74.40</td>\n",
       "      <td>306.6</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>3186-AJIEK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents     MultipleLines  \\\n",
       "0     7590-VHVEG  Female              0     Yes         No  No phone service   \n",
       "1     5575-GNVDE    Male              0      No         No                No   \n",
       "2     3668-QPYBK    Male              0      No         No                No   \n",
       "3     7795-CFOCW    Male              0      No         No  No phone service   \n",
       "4     9237-HQITU  Female              0      No         No                No   \n",
       "...          ...     ...            ...     ...        ...               ...   \n",
       "7038  6840-RESVB    Male              0     Yes        Yes               Yes   \n",
       "7039  2234-XADUH  Female              0     Yes        Yes               Yes   \n",
       "7040  4801-JZAZL  Female              0     Yes        Yes  No phone service   \n",
       "7041  8361-LTMKD    Male              1     Yes         No               Yes   \n",
       "7042  3186-AJIEK    Male              0      No         No                No   \n",
       "\n",
       "     InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport  \\\n",
       "0                DSL             No          Yes               No          No   \n",
       "1                DSL            Yes           No              Yes          No   \n",
       "2                DSL            Yes          Yes               No          No   \n",
       "3                DSL            Yes           No              Yes         Yes   \n",
       "4        Fiber optic             No           No               No          No   \n",
       "...              ...            ...          ...              ...         ...   \n",
       "7038             DSL            Yes           No              Yes         Yes   \n",
       "7039     Fiber optic             No          Yes              Yes          No   \n",
       "7040             DSL            Yes           No               No          No   \n",
       "7041     Fiber optic             No           No               No          No   \n",
       "7042     Fiber optic            Yes           No              Yes         Yes   \n",
       "\n",
       "     StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0             No              No  Month-to-month              Yes   \n",
       "1             No              No        One year               No   \n",
       "2             No              No  Month-to-month              Yes   \n",
       "3             No              No        One year               No   \n",
       "4             No              No  Month-to-month              Yes   \n",
       "...          ...             ...             ...              ...   \n",
       "7038         Yes             Yes        One year              Yes   \n",
       "7039         Yes             Yes        One year              Yes   \n",
       "7040          No              No  Month-to-month              Yes   \n",
       "7041          No              No  Month-to-month              Yes   \n",
       "7042         Yes             Yes        Two year              Yes   \n",
       "\n",
       "                  PaymentMethod  MonthlyCharges TotalCharges Churn  \n",
       "0              Electronic check           29.85        29.85    No  \n",
       "1                  Mailed check           56.95       1889.5    No  \n",
       "2                  Mailed check           53.85       108.15   Yes  \n",
       "3     Bank transfer (automatic)           42.30      1840.75    No  \n",
       "4              Electronic check           70.70       151.65   Yes  \n",
       "...                         ...             ...          ...   ...  \n",
       "7038               Mailed check           84.80       1990.5    No  \n",
       "7039    Credit card (automatic)          103.20       7362.9    No  \n",
       "7040           Electronic check           29.60       346.45    No  \n",
       "7041               Mailed check           74.40        306.6   Yes  \n",
       "7042  Bank transfer (automatic)          105.65       6844.5    No  \n",
       "\n",
       "[7043 rows x 19 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#上面没有缺失值，但是如果有缺失值按以下处理\n",
    "# df.dropna(axis=1,thresh=df.shape[0]*0.8) \n",
    "# 因为视频中的文件做了修改，把tenure、PhoneService两列的数据做成了缺失值>20%,我这里为了保持一致，假设这两列缺失值也超过了20%，所以要删\n",
    "df_drop = df.drop(axis=1,columns=['tenure','PhoneService'])\n",
    "df_drop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09a0f042",
   "metadata": {},
   "source": [
    "4.对缺失值未超过20%的特征进行填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c4edea9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 说明：视频中把InternetService、OnlineSecurity、OnlineBackup、DeviceProtection这几列的数据做了修改，让这几列有了缺失值，但是没超过20%，对这些进行填充\n",
    "# 这里为了和视频保持一致，也做这样的填充处理\n",
    "# 这里做填充可以用三步走的方法，也可以直接用replace填充\n",
    "# 三步走：\n",
    "'''\n",
    "from sklearn.impute import SimpleImputer\n",
    "model_si1 = SimpleImputer(missing_values=np.nan,strategy='most_frequent')\n",
    "df_si1 = model_si1.fit_transform(X=df_drop[['InternetService','OnlineSecurity','OnlineBackup','DeviceProtection']]) #X要求的数据为二维的数据所以使用两个方括号\n",
    "df_si1 = pd.DataFrame(data=df_si1,columns=['InternetService','OnlineSecurity','OnlineBackup','DeviceProtection'])\n",
    "df_drop[['InternetService','OnlineSecurity','OnlineBackup','DeviceProtection']] = df_si1[['InternetService','OnlineSecurity','OnlineBackup','DeviceProtection']]\n",
    "df_drop\n",
    "'''\n",
    "#用replace\n",
    "list_col = ['InternetService','OnlineSecurity','OnlineBackup','DeviceProtection']\n",
    "for col in list_col:\n",
    "    # 把缺失值用众数替换\n",
    "    df_drop[col] = df_drop[col].replace(np.nan,df_drop[col].mode()[0]) #众数可能有多个，所以带下标取第一个"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "20bd06a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID          0.0\n",
       "gender              0.0\n",
       "SeniorCitizen       0.0\n",
       "Partner             0.0\n",
       "Dependents          0.0\n",
       "MultipleLines       0.0\n",
       "InternetService     0.0\n",
       "OnlineSecurity      0.0\n",
       "OnlineBackup        0.0\n",
       "DeviceProtection    0.0\n",
       "TechSupport         0.0\n",
       "StreamingTV         0.0\n",
       "StreamingMovies     0.0\n",
       "Contract            0.0\n",
       "PaperlessBilling    0.0\n",
       "PaymentMethod       0.0\n",
       "MonthlyCharges      0.0\n",
       "TotalCharges        0.0\n",
       "Churn               0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drop.isnull().sum()/df_drop.shape[0] * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5928bc34",
   "metadata": {},
   "source": [
    "5.通过柱状图查看目标列的分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "267648cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rOPX19c5VI4/Ho/b2djU2Nl5w5sSJE11et6GhocvVp8+KiIhwPv129gEAAK5O3Qqit956S+3t7V22f/rpp/rd7373hRZk27ba2to0cuRIeTwelZWVOfva29tVXl6uCRMmSJJSUlI0cODAoJm6ujodPHjQmUlLS1MgEFBlZaUzs3fvXgUCAWcGAACYLfxyht99913nf7/33ntBV286Ozvl8/n0d3/3d5d8vIcfflgzZsxQQkKCWlpaVFJSorfeeks+n08ul0t5eXkqKChQYmKiEhMTVVBQoMGDBysnJ0eSZFmW5s6dq6VLl2rYsGGKjY3VsmXLlJycrIyMDEnSmDFjNH36dM2bN08bNmyQJM2fP1+ZmZmX/AkzAABwdbusILrtttvkcrnkcrnO+9ZYZGSknn322Us+3okTJ5Sbm6u6ujpZlqVx48bJ5/NpypQpkqTly5ertbVVCxYsUGNjo1JTU1VaWqro6GjnGGvXrlV4eLiys7PV2tqq9PR0FRcXKywszJnZunWrFi9e7HwaLSsrS0VFRZdz6gAA4Cp2Wd9D9Je//EW2bevGG29UZWWl4uLinH2DBg1SfHx8UIhcTfgeIiB0+B4iAN11qb+/L+sK0YgRIyRJZ86c+WKrAwAA6EMuK4g+6w9/+IPeeust1dfXdwmkxx577AsvDAAAoLd0K4g2btyo73znOxo+fLg8Hk+X7/whiAAAQH/SrSD60Y9+pB//+MfO3xgDAADoz7r1PUSNjY267777rvRaAAAAQqJbQXTfffeptLT0Sq8FAAAgJLr1ltlNN92kRx99VBUVFUpOTtbAgQOD9i9evPiKLA4AAKA3dCuInn/+eQ0ZMkTl5eUqLy8P2udyuQgiAADQr3QriGpra6/0OgAAAEKmW/cQAQAAXE26dYVozpw5F9z/4osvdmsxAAAAodCtIGpsbAx63tHRoYMHD6qpqem8f/QVAACgL+tWEO3YsaPLtjNnzmjBggW68cYbv/CiAAAAetMVu4dowIAB+rd/+zetXbv2Sh0SAACgV1zRm6r/+Mc/6q9//euVPCQAAECP69ZbZkuWLAl6btu26urq9Morr2jWrFlXZGEAAAC9pVtB9M477wQ9HzBggOLi4vT0009f9BNoAAAAfU23gujNN9+80usAAAAImW4F0VkNDQ06fPiwXC6XRo0apbi4uCu1LgAAgF7TrZuqT58+rTlz5ui6667THXfcoa9//evyer2aO3euPvnkkyu9RgAAgB7VrSBasmSJysvL9T//8z9qampSU1OTfvWrX6m8vFxLly690msEAADoUd16y2z79u16+eWXNXnyZGfbXXfdpcjISGVnZ2v9+vVXan0AAAA9rltXiD755BO53e4u2+Pj43nLDAAA9DvdCqK0tDT94Ac/0Keffupsa21t1eOPP660tLQrtjgAAIDe0K23zNatW6cZM2bo+uuv16233iqXy6Xq6mpFRESotLT0Sq8RAACgR3UriJKTk3XkyBFt2bJF77//vmzb1gMPPKCZM2cqMjLySq8RAACgR3UriAoLC+V2uzVv3ryg7S+++KIaGhq0YsWKK7I4AACA3tCte4g2bNigm2++ucv2W265Rf/1X//1hRcFAADQm7oVRH6/X9ddd12X7XFxcaqrq/vCiwIAAOhN3QqihIQEvf322122v/322/J6vV94UQAAAL2pW/cQfetb31JeXp46Ojp05513SpLeeOMNLV++nG+qBgAA/U63gmj58uU6efKkFixYoPb2dknSNddcoxUrVmjlypVXdIEAAAA9rVtB5HK5tGrVKj366KOqqalRZGSkEhMTFRERcaXXBwAA0OO6FURnDRkyRF/96lev1FoAAABCols3VQMAAFxNCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYL6RBVFhYqK9+9auKjo5WfHy8/umf/kmHDx8OmrFtW/n5+fJ6vYqMjNTkyZN16NChoJm2tjYtWrRIw4cPV1RUlLKysnT8+PGgmcbGRuXm5sqyLFmWpdzcXDU1NfX0KQIAgH4gpEFUXl6uhQsXqqKiQmVlZfrrX/+qqVOn6vTp087M6tWrtWbNGhUVFWnfvn3yeDyaMmWKWlpanJm8vDzt2LFDJSUl2rVrl06dOqXMzEx1dnY6Mzk5OaqurpbP55PP51N1dbVyc3N79XwBAEDf5LJt2w71Is5qaGhQfHy8ysvLdccdd8i2bXm9XuXl5WnFihWS/nY1yO12a9WqVXrooYcUCAQUFxenzZs36/7775ckffjhh0pISNCrr76qadOmqaamRmPHjlVFRYVSU1MlSRUVFUpLS9P777+v0aNHX3Rtzc3NsixLgUBAMTExPfbfIOX7L/XYsYH+quqpb4Z6CQD6qUv9/d2n7iEKBAKSpNjYWElSbW2t/H6/pk6d6sxERERo0qRJ2r17tySpqqpKHR0dQTNer1dJSUnOzJ49e2RZlhNDkjR+/HhZluXMnKutrU3Nzc1BDwAAcHXqM0Fk27aWLFmiv//7v1dSUpIkye/3S5LcbnfQrNvtdvb5/X4NGjRIQ4cOveBMfHx8l9eMj493Zs5VWFjo3G9kWZYSEhK+2AkCAIA+q88E0Xe/+129++67+vnPf95ln8vlCnpu23aXbec6d+Z88xc6zsqVKxUIBJzHsWPHLuU0AABAP9QngmjRokX69a9/rTfffFPXX3+9s93j8UhSl6s49fX1zlUjj8ej9vZ2NTY2XnDmxIkTXV63oaGhy9WnsyIiIhQTExP0AAAAV6eQBpFt2/rud7+rX/7yl/q///s/jRw5Mmj/yJEj5fF4VFZW5mxrb29XeXm5JkyYIElKSUnRwIEDg2bq6up08OBBZyYtLU2BQECVlZXOzN69exUIBJwZAABgrvBQvvjChQu1bds2/epXv1J0dLRzJciyLEVGRsrlcikvL08FBQVKTExUYmKiCgoKNHjwYOXk5Dizc+fO1dKlSzVs2DDFxsZq2bJlSk5OVkZGhiRpzJgxmj59uubNm6cNGzZIkubPn6/MzMxL+oQZAAC4uoU0iNavXy9Jmjx5ctD2n/3sZ5o9e7Ykafny5WptbdWCBQvU2Nio1NRUlZaWKjo62plfu3atwsPDlZ2drdbWVqWnp6u4uFhhYWHOzNatW7V48WLn02hZWVkqKirq2RMEAAD9Qp/6HqK+jO8hAkKH7yEC0F398nuIAAAAQoEgAgAAxgvpPUQAYJKjTySHeglAn3PDYwdCvQRJXCECAAAgiAAAAAgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxQhpEv/3tb3X33XfL6/XK5XJp586dQftt21Z+fr68Xq8iIyM1efJkHTp0KGimra1NixYt0vDhwxUVFaWsrCwdP348aKaxsVG5ubmyLEuWZSk3N1dNTU09fHYAAKC/CGkQnT59WrfeequKiorOu3/16tVas2aNioqKtG/fPnk8Hk2ZMkUtLS3OTF5ennbs2KGSkhLt2rVLp06dUmZmpjo7O52ZnJwcVVdXy+fzyefzqbq6Wrm5uT1+fgAAoH8ID+WLz5gxQzNmzDjvPtu2tW7dOj3yyCO69957JUmbNm2S2+3Wtm3b9NBDDykQCOiFF17Q5s2blZGRIUnasmWLEhIS9Prrr2vatGmqqamRz+dTRUWFUlNTJUkbN25UWlqaDh8+rNGjR/fOyQIAgD6rz95DVFtbK7/fr6lTpzrbIiIiNGnSJO3evVuSVFVVpY6OjqAZr9erpKQkZ2bPnj2yLMuJIUkaP368LMtyZs6nra1Nzc3NQQ8AAHB16rNB5Pf7JUlutztou9vtdvb5/X4NGjRIQ4cOveBMfHx8l+PHx8c7M+dTWFjo3HNkWZYSEhK+0PkAAIC+q88G0VkulyvouW3bXbad69yZ881f7DgrV65UIBBwHseOHbvMlQMAgP6izwaRx+ORpC5Xcerr652rRh6PR+3t7WpsbLzgzIkTJ7ocv6GhocvVp8+KiIhQTExM0AMAAFyd+mwQjRw5Uh6PR2VlZc629vZ2lZeXa8KECZKklJQUDRw4MGimrq5OBw8edGbS0tIUCARUWVnpzOzdu1eBQMCZAQAAZgvpp8xOnTqlDz74wHleW1ur6upqxcbG6oYbblBeXp4KCgqUmJioxMREFRQUaPDgwcrJyZEkWZaluXPnaunSpRo2bJhiY2O1bNkyJScnO586GzNmjKZPn6558+Zpw4YNkqT58+crMzOTT5gBAABJIQ6i/fv36xvf+IbzfMmSJZKkWbNmqbi4WMuXL1dra6sWLFigxsZGpaamqrS0VNHR0c7PrF27VuHh4crOzlZra6vS09NVXFyssLAwZ2br1q1avHix82m0rKysz/3uIwAAYB6Xbdt2qBfRHzQ3N8uyLAUCgR69nyjl+y/12LGB/qrqqW+GeglXxNEnkkO9BKDPueGxAz16/Ev9/d1n7yECAADoLQQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHhGBdFzzz2nkSNH6pprrlFKSop+97vfhXpJAACgDzAmiP77v/9beXl5euSRR/TOO+/o61//umbMmKGjR4+GemkAACDEjAmiNWvWaO7cufrWt76lMWPGaN26dUpISND69etDvTQAABBi4aFeQG9ob29XVVWV/v3f/z1o+9SpU7V79+7z/kxbW5va2tqc54FAQJLU3NzccwuV1NnW2qPHB/qjnv5311taPu0M9RKAPqen/32fPb5t2xecMyKIPvroI3V2dsrtdgdtd7vd8vv95/2ZwsJCPf744122JyQk9MgaAXw+69lvh3oJAHpKodUrL9PS0iLL+vzXMiKIznK5XEHPbdvusu2slStXasmSJc7zM2fO6OTJkxo2bNjn/gyuHs3NzUpISNCxY8cUExMT6uUAuIL4920W27bV0tIir9d7wTkjgmj48OEKCwvrcjWovr6+y1WjsyIiIhQRERG07dprr+2pJaKPiomJ4f8wgasU/77NcaErQ2cZcVP1oEGDlJKSorKysqDtZWVlmjBhQohWBQAA+gojrhBJ0pIlS5Sbm6vbb79daWlpev7553X06FF9+9vcmwAAgOmMCaL7779fH3/8sZ544gnV1dUpKSlJr776qkaMGBHqpaEPioiI0A9+8IMub5sC6P/4943zcdkX+xwaAADAVc6Ie4gAAAAuhCACAADGI4gAAIDxCCIAAGA8ggjGmj17tlwul5588smg7Tt37uTbyIF+yLZtZWRkaNq0aV32Pffcc7IsS0ePHg3BytAfEEQw2jXXXKNVq1apsbEx1EsB8AW5XC797Gc/0969e7VhwwZne21trVasWKFnnnlGN9xwQwhXiL6MIILRMjIy5PF4VFhY+Lkz27dv1y233KKIiAh96Utf0tNPP92LKwRwORISEvTMM89o2bJlqq2tlW3bmjt3rtLT0/W1r31Nd911l4YMGSK3263c3Fx99NFHzs++/PLLSk5OVmRkpIYNG6aMjAydPn06hGeD3kQQwWhhYWEqKCjQs88+q+PHj3fZX1VVpezsbD3wwAM6cOCA8vPz9eijj6q4uLj3FwvgksyaNUvp6en613/9VxUVFengwYN65plnNGnSJN12223av3+/fD6fTpw4oezsbElSXV2dHnzwQc2ZM0c1NTV66623dO+994qv6jMHX8wIY82ePVtNTU3auXOn0tLSNHbsWL3wwgvauXOn7rnnHtm2rZkzZ6qhoUGlpaXOzy1fvlyvvPKKDh06FMLVA7iQ+vp6JSUl6eOPP9bLL7+sd955R3v37tVrr73mzBw/flwJCQk6fPiwTp06pZSUFP35z3/mLxgYiitEgKRVq1Zp06ZNeu+994K219TUaOLEiUHbJk6cqCNHjqizs7M3lwjgMsTHx2v+/PkaM2aM7rnnHlVVVenNN9/UkCFDnMfNN98sSfrjH/+oW2+9Venp6UpOTtZ9992njRs3cm+hYQgiQNIdd9yhadOm6eGHHw7abtt2l0+ccVEV6B/Cw8MVHv63P9l55swZ3X333aqurg56HDlyRHfccYfCwsJUVlam3/zmNxo7dqyeffZZjR49WrW1tSE+C/QWY/64K3AxTz75pG677TaNGjXK2TZ27Fjt2rUraG737t0aNWqUwsLCenuJALrpK1/5irZv364vfelLTiSdy+VyaeLEiZo4caIee+wxjRgxQjt27NCSJUt6ebUIBa4QAf9fcnKyZs6cqWeffdbZtnTpUr3xxhv64Q9/qD/84Q/atGmTioqKtGzZshCuFMDlWrhwoU6ePKkHH3xQlZWV+tOf/qTS0lLNmTNHnZ2d2rt3rwoKCrR//34dPXpUv/zlL9XQ0KAxY8aEeunoJQQR8Bk//OEPg94S+8pXvqJf/OIXKikpUVJSkh577DE98cQTmj17dugWCeCyeb1evf322+rs7NS0adOUlJSk733ve7IsSwMGDFBMTIx++9vf6q677tKoUaP0H//xH3r66ac1Y8aMUC8dvYRPmQEAAONxhQgAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIwFXN5XJp586doV4GgD6OIALQr/n9fi1atEg33nijIiIilJCQoLvvvltvvPFGqJcGoB/hr90D6Lf+/Oc/a+LEibr22mu1evVqjRs3Th0dHXrttde0cOFCvf/++z3yuh0dHRo4cGCPHBtAaHCFCEC/tWDBArlcLlVWVupf/uVfNGrUKN1yyy1asmSJKioqnLmPPvpI99xzjwYPHqzExET9+te/dvYVFxfr2muvDTruzp075XK5nOf5+fm67bbb9OKLLzpXomzblsvl0k9/+tPPPTaA/oMgAtAvnTx5Uj6fTwsXLlRUVFSX/Z+NnMcff1zZ2dl69913ddddd2nmzJk6efLkZb3eBx98oF/84hfavn27qqurr+ixAYQeQQSgX/rggw9k27Zuvvnmi87Onj1bDz74oG666SYVFBTo9OnTqqysvKzXa29v1+bNm/XlL39Z48aNc64gXYljAwg9gghAv2TbtiQFvbX1ecaNG+f876ioKEVHR6u+vv6yXm/EiBGKi4vrkWMDCD2CCEC/lJiYKJfLpZqamovOnnsDtMvl0pkzZyRJAwYMcOLqrI6Oji7HON/bchc7NoD+gyAC0C/FxsZq2rRp+s///E+dPn26y/6mpqZLOk5cXJxaWlqCjvHZe4QAmIEgAtBvPffcc+rs7NTXvvY1bd++XUeOHFFNTY1+8pOfKC0t7ZKOkZqaqsGDB+vhhx/WBx98oG3btqm4uLhnFw6gzyGIAPRbI0eO1O9//3t94xvf0NKlS5WUlKQpU6bojTfe0Pr16y/pGLGxsdqyZYteffVVJScn6+c//7ny8/N7duEA+hyXfe6b5wAAAIbhChEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADj/T9IpRSlTNKWzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.countplot(x=df_drop['Churn'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "963c68d5",
   "metadata": {},
   "source": [
    "6.通过饼状图查看目标列的分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d3ed1ef0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(x=df_drop['Churn'].value_counts(),labels=['No','Yes'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f8231f5",
   "metadata": {},
   "source": [
    "7.查看相关性系数(皮尔逊相关性系数)，并绘制热力图，删除相关性大于0.8的列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a719e966",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 特征选择处理的都是数值类型的数据，做特征选择之前，我们通过下面的人为分析发现 TotalCharges这列应该是数值类型，但有极个别的异常值，\n",
    "# 我们可以选择先处理成数值类型的，即转型，如果转型失败，那么得先处理异常值，这里用众数来替换异常值\n",
    "df_drop['TotalCharges'] = df_drop['TotalCharges'].replace(' ',df_drop['TotalCharges'].mode()[1]) #这里mode(0)为' ',所以不用这个来替换\n",
    "df_drop['TotalCharges'] = df_drop['TotalCharges'].astype(np.float64) #发现报错：ValueError: could not convert string to float: ' '"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "82980385",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 19 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   MultipleLines     7043 non-null   object \n",
      " 6   InternetService   7043 non-null   object \n",
      " 7   OnlineSecurity    7043 non-null   object \n",
      " 8   OnlineBackup      7043 non-null   object \n",
      " 9   DeviceProtection  7043 non-null   object \n",
      " 10  TechSupport       7043 non-null   object \n",
      " 11  StreamingTV       7043 non-null   object \n",
      " 12  StreamingMovies   7043 non-null   object \n",
      " 13  Contract          7043 non-null   object \n",
      " 14  PaperlessBilling  7043 non-null   object \n",
      " 15  PaymentMethod     7043 non-null   object \n",
      " 16  MonthlyCharges    7043 non-null   float64\n",
      " 17  TotalCharges      7043 non-null   float64\n",
      " 18  Churn             7043 non-null   object \n",
      "dtypes: float64(2), int64(1), object(16)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_drop.info() #TotalCharges列已转为float类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "131b7b82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除数据，删除不相关的列,删除人为分析层面无意义的数据，因为相关性系数特征选择只能做数值类型的，非数值类型的做不了，如果是\n",
    "# 其他object等其他类型，可以人为判断分析和目标列有没有关系\n",
    "#这里人为分析出：customerID和目标列一定没关系，所以可以删除这列\n",
    "# df_drop.drop(axis=1,columns=['customerID'],inplace=True)\n",
    "# 还有种删除方法\n",
    "del df_drop['customerID']\n",
    "# df_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a329bfc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>84.80</td>\n",
       "      <td>1990.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>103.20</td>\n",
       "      <td>7362.90</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.60</td>\n",
       "      <td>346.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>74.40</td>\n",
       "      <td>306.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  SeniorCitizen Partner Dependents     MultipleLines  \\\n",
       "0     Female              0     Yes         No  No phone service   \n",
       "1       Male              0      No         No                No   \n",
       "2       Male              0      No         No                No   \n",
       "3       Male              0      No         No  No phone service   \n",
       "4     Female              0      No         No                No   \n",
       "...      ...            ...     ...        ...               ...   \n",
       "7038    Male              0     Yes        Yes               Yes   \n",
       "7039  Female              0     Yes        Yes               Yes   \n",
       "7040  Female              0     Yes        Yes  No phone service   \n",
       "7041    Male              1     Yes         No               Yes   \n",
       "7042    Male              0      No         No                No   \n",
       "\n",
       "     InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport  \\\n",
       "0                DSL             No          Yes               No          No   \n",
       "1                DSL            Yes           No              Yes          No   \n",
       "2                DSL            Yes          Yes               No          No   \n",
       "3                DSL            Yes           No              Yes         Yes   \n",
       "4        Fiber optic             No           No               No          No   \n",
       "...              ...            ...          ...              ...         ...   \n",
       "7038             DSL            Yes           No              Yes         Yes   \n",
       "7039     Fiber optic             No          Yes              Yes          No   \n",
       "7040             DSL            Yes           No               No          No   \n",
       "7041     Fiber optic             No           No               No          No   \n",
       "7042     Fiber optic            Yes           No              Yes         Yes   \n",
       "\n",
       "     StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0             No              No  Month-to-month              Yes   \n",
       "1             No              No        One year               No   \n",
       "2             No              No  Month-to-month              Yes   \n",
       "3             No              No        One year               No   \n",
       "4             No              No  Month-to-month              Yes   \n",
       "...          ...             ...             ...              ...   \n",
       "7038         Yes             Yes        One year              Yes   \n",
       "7039         Yes             Yes        One year              Yes   \n",
       "7040          No              No  Month-to-month              Yes   \n",
       "7041          No              No  Month-to-month              Yes   \n",
       "7042         Yes             Yes        Two year              Yes   \n",
       "\n",
       "                  PaymentMethod  MonthlyCharges  TotalCharges Churn  \n",
       "0              Electronic check           29.85         29.85    No  \n",
       "1                  Mailed check           56.95       1889.50    No  \n",
       "2                  Mailed check           53.85        108.15   Yes  \n",
       "3     Bank transfer (automatic)           42.30       1840.75    No  \n",
       "4              Electronic check           70.70        151.65   Yes  \n",
       "...                         ...             ...           ...   ...  \n",
       "7038               Mailed check           84.80       1990.50    No  \n",
       "7039    Credit card (automatic)          103.20       7362.90    No  \n",
       "7040           Electronic check           29.60        346.45    No  \n",
       "7041               Mailed check           74.40        306.60   Yes  \n",
       "7042  Bank transfer (automatic)          105.65       6844.50    No  \n",
       "\n",
       "[7043 rows x 18 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drop # 已经删掉customerID列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "90fb827a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
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       "      <th>StreamingTV</th>\n",
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       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>Churn</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043</td>\n",
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       "      <th>unique</th>\n",
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>3555</td>\n",
       "      <td>3641</td>\n",
       "      <td>4933</td>\n",
       "      <td>3390</td>\n",
       "      <td>3096</td>\n",
       "      <td>3498</td>\n",
       "      <td>3088</td>\n",
       "      <td>3095</td>\n",
       "      <td>3473</td>\n",
       "      <td>2810</td>\n",
       "      <td>2785</td>\n",
       "      <td>3875</td>\n",
       "      <td>4171</td>\n",
       "      <td>2365</td>\n",
       "      <td>5174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       gender Partner Dependents MultipleLines InternetService OnlineSecurity  \\\n",
       "count    7043    7043       7043          7043            7043           7043   \n",
       "unique      2       2          2             3               3              3   \n",
       "top      Male      No         No            No     Fiber optic             No   \n",
       "freq     3555    3641       4933          3390            3096           3498   \n",
       "\n",
       "       OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies  \\\n",
       "count          7043             7043        7043        7043            7043   \n",
       "unique            3                3           3           3               3   \n",
       "top              No               No          No          No              No   \n",
       "freq           3088             3095        3473        2810            2785   \n",
       "\n",
       "              Contract PaperlessBilling     PaymentMethod Churn  \n",
       "count             7043             7043              7043  7043  \n",
       "unique               3                2                 4     2  \n",
       "top     Month-to-month              Yes  Electronic check    No  \n",
       "freq              3875             4171              2365  5174  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 有些人为分析不出来，但是也是非数值类型的数据，可以用特征编码转为数值类型的，然后再用相关性系数看\n",
    "# 我们可以用desribe('O')来分析是用哪种特征编码\n",
    "\n",
    "# include='O'中的O不是零，而是大写字母O\n",
    "# 其中答应出来的信息中索引代表的含义：\n",
    "# count:代表某个列一共有多少行数据 \n",
    "# unique：代表不重复的值有多少个，这个非常关键，可以通过这个信息查看这个列有几个值，来断定这列要不要做特征编码，来把object类型转为数值型\n",
    "# 而同时也可以判定我们到底是做独热编码还是label-encoding编码，如果列的值只有两个，那么做label-encoding编码，也就是这个列属于二元属性\n",
    "df_drop.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e8debb8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 目标列一般不做特征编码  ,做特征编码，把object类型的列都拿出来\n",
    "list_c = []\n",
    "df_drop.dtypes\n",
    "# for col in df_drop.columns:\n",
    "#     if df_drop[col].dtypes == 'object' and col!= 'Churn':\n",
    "#         list_c.append(col)\n",
    "# 也可以用列表表达式\n",
    "list_c = [col for col in df_drop.columns if df_drop[col].dtypes == 'object' and col!= 'Churn']\n",
    "for col in list_c:\n",
    "#     factorize是pandas中提供的一个方法，会把所有的值依次映射成0,1,2,3,4,...,所谓依次就是这一列有几个去重后的值，那么就按顺序映射成0,1,2,3,4,...,\n",
    "    df_drop[col] = pd.factorize(df_drop[col])[0] # 取下标为0就是取去映射后的这一列数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0bd51c90",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>84.80</td>\n",
       "      <td>1990.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>103.20</td>\n",
       "      <td>7362.90</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.60</td>\n",
       "      <td>346.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>74.40</td>\n",
       "      <td>306.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  SeniorCitizen  Partner  Dependents  MultipleLines  \\\n",
       "0          0              0        0           0              0   \n",
       "1          1              0        1           0              1   \n",
       "2          1              0        1           0              1   \n",
       "3          1              0        1           0              0   \n",
       "4          0              0        1           0              1   \n",
       "...      ...            ...      ...         ...            ...   \n",
       "7038       1              0        0           1              2   \n",
       "7039       0              0        0           1              2   \n",
       "7040       0              0        0           1              0   \n",
       "7041       1              1        0           0              2   \n",
       "7042       1              0        1           0              1   \n",
       "\n",
       "      InternetService  OnlineSecurity  OnlineBackup  DeviceProtection  \\\n",
       "0                   0               0             0                 0   \n",
       "1                   0               1             1                 1   \n",
       "2                   0               1             0                 0   \n",
       "3                   0               1             1                 1   \n",
       "4                   1               0             1                 0   \n",
       "...               ...             ...           ...               ...   \n",
       "7038                0               1             1                 1   \n",
       "7039                1               0             0                 1   \n",
       "7040                0               1             1                 0   \n",
       "7041                1               0             1                 0   \n",
       "7042                1               1             1                 1   \n",
       "\n",
       "      TechSupport  StreamingTV  StreamingMovies  Contract  PaperlessBilling  \\\n",
       "0               0            0                0         0                 0   \n",
       "1               0            0                0         1                 1   \n",
       "2               0            0                0         0                 0   \n",
       "3               1            0                0         1                 1   \n",
       "4               0            0                0         0                 0   \n",
       "...           ...          ...              ...       ...               ...   \n",
       "7038            1            1                1         1                 0   \n",
       "7039            0            1                1         1                 0   \n",
       "7040            0            0                0         0                 0   \n",
       "7041            0            0                0         0                 0   \n",
       "7042            1            1                1         2                 0   \n",
       "\n",
       "      PaymentMethod  MonthlyCharges  TotalCharges Churn  \n",
       "0                 0           29.85         29.85    No  \n",
       "1                 1           56.95       1889.50    No  \n",
       "2                 1           53.85        108.15   Yes  \n",
       "3                 2           42.30       1840.75    No  \n",
       "4                 0           70.70        151.65   Yes  \n",
       "...             ...             ...           ...   ...  \n",
       "7038              1           84.80       1990.50    No  \n",
       "7039              3          103.20       7362.90    No  \n",
       "7040              0           29.60        346.45    No  \n",
       "7041              1           74.40        306.60   Yes  \n",
       "7042              2          105.65       6844.50    No  \n",
       "\n",
       "[7043 rows x 18 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7b2b0063",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            7043 non-null   int64  \n",
      " 1   SeniorCitizen     7043 non-null   int64  \n",
      " 2   Partner           7043 non-null   int64  \n",
      " 3   Dependents        7043 non-null   int64  \n",
      " 4   MultipleLines     7043 non-null   int64  \n",
      " 5   InternetService   7043 non-null   int64  \n",
      " 6   OnlineSecurity    7043 non-null   int64  \n",
      " 7   OnlineBackup      7043 non-null   int64  \n",
      " 8   DeviceProtection  7043 non-null   int64  \n",
      " 9   TechSupport       7043 non-null   int64  \n",
      " 10  StreamingTV       7043 non-null   int64  \n",
      " 11  StreamingMovies   7043 non-null   int64  \n",
      " 12  Contract          7043 non-null   int64  \n",
      " 13  PaperlessBilling  7043 non-null   int64  \n",
      " 14  PaymentMethod     7043 non-null   int64  \n",
      " 15  MonthlyCharges    7043 non-null   float64\n",
      " 16  TotalCharges      7043 non-null   float64\n",
      " 17  Churn             7043 non-null   object \n",
      "dtypes: float64(2), int64(15), object(1)\n",
      "memory usage: 990.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_drop.info() # 现在都变成了int类型了，那么这些之前非数值类型的列也可以做相关系数了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ced96e99",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_31560\\1478908587.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  cor = df_drop.corr()\n"
     ]
    },
    {
     "data": {
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       "      <td>0.220173</td>\n",
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       "      <td>-0.317518</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.062100</td>\n",
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       "      <td>-0.154370</td>\n",
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       "      <td>0.003183</td>\n",
       "      <td>-0.710477</td>\n",
       "      <td>-0.537214</td>\n",
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       "      <td>0.156439</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.767970</td>\n",
       "      <td>0.763279</td>\n",
       "      <td>0.766821</td>\n",
       "      <td>0.390216</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.191746</td>\n",
       "      <td>-0.513440</td>\n",
       "      <td>-0.078601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport</th>\n",
       "      <td>0.000985</td>\n",
       "      <td>-0.223770</td>\n",
       "      <td>-0.069072</td>\n",
       "      <td>0.180832</td>\n",
       "      <td>-0.066684</td>\n",
       "      <td>0.609795</td>\n",
       "      <td>0.791225</td>\n",
       "      <td>0.617003</td>\n",
       "      <td>0.767970</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.737578</td>\n",
       "      <td>0.737123</td>\n",
       "      <td>0.418440</td>\n",
       "      <td>0.310749</td>\n",
       "      <td>0.216878</td>\n",
       "      <td>-0.597594</td>\n",
       "      <td>-0.142166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV</th>\n",
       "      <td>0.001156</td>\n",
       "      <td>-0.130130</td>\n",
       "      <td>-0.080127</td>\n",
       "      <td>0.140395</td>\n",
       "      <td>0.030195</td>\n",
       "      <td>0.712890</td>\n",
       "      <td>0.701976</td>\n",
       "      <td>0.604117</td>\n",
       "      <td>0.763279</td>\n",
       "      <td>0.737578</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.809608</td>\n",
       "      <td>0.327951</td>\n",
       "      <td>0.203907</td>\n",
       "      <td>0.117618</td>\n",
       "      <td>-0.423067</td>\n",
       "      <td>-0.076855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies</th>\n",
       "      <td>-0.000191</td>\n",
       "      <td>-0.120802</td>\n",
       "      <td>-0.075779</td>\n",
       "      <td>0.125820</td>\n",
       "      <td>0.028187</td>\n",
       "      <td>0.709020</td>\n",
       "      <td>0.704984</td>\n",
       "      <td>0.606863</td>\n",
       "      <td>0.766821</td>\n",
       "      <td>0.737123</td>\n",
       "      <td>0.809608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.330993</td>\n",
       "      <td>0.211818</td>\n",
       "      <td>0.123869</td>\n",
       "      <td>-0.424598</td>\n",
       "      <td>-0.073155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>0.000126</td>\n",
       "      <td>-0.142554</td>\n",
       "      <td>-0.294806</td>\n",
       "      <td>0.243187</td>\n",
       "      <td>0.083343</td>\n",
       "      <td>0.099721</td>\n",
       "      <td>0.389978</td>\n",
       "      <td>0.035407</td>\n",
       "      <td>0.390216</td>\n",
       "      <td>0.418440</td>\n",
       "      <td>0.327951</td>\n",
       "      <td>0.330993</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.176733</td>\n",
       "      <td>0.358913</td>\n",
       "      <td>-0.074195</td>\n",
       "      <td>0.446882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>0.011754</td>\n",
       "      <td>-0.156530</td>\n",
       "      <td>-0.014877</td>\n",
       "      <td>0.111377</td>\n",
       "      <td>-0.133255</td>\n",
       "      <td>0.138625</td>\n",
       "      <td>0.334003</td>\n",
       "      <td>0.260715</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.310749</td>\n",
       "      <td>0.203907</td>\n",
       "      <td>0.211818</td>\n",
       "      <td>0.176733</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.101480</td>\n",
       "      <td>-0.352150</td>\n",
       "      <td>-0.158568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod</th>\n",
       "      <td>-0.005209</td>\n",
       "      <td>-0.093704</td>\n",
       "      <td>-0.133115</td>\n",
       "      <td>0.123844</td>\n",
       "      <td>0.025676</td>\n",
       "      <td>0.008124</td>\n",
       "      <td>0.213800</td>\n",
       "      <td>0.003183</td>\n",
       "      <td>0.191746</td>\n",
       "      <td>0.216878</td>\n",
       "      <td>0.117618</td>\n",
       "      <td>0.123869</td>\n",
       "      <td>0.358913</td>\n",
       "      <td>0.101480</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.074353</td>\n",
       "      <td>0.222401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>-0.014569</td>\n",
       "      <td>0.220173</td>\n",
       "      <td>-0.096848</td>\n",
       "      <td>-0.113890</td>\n",
       "      <td>0.490700</td>\n",
       "      <td>-0.323260</td>\n",
       "      <td>-0.621227</td>\n",
       "      <td>-0.710477</td>\n",
       "      <td>-0.513440</td>\n",
       "      <td>-0.597594</td>\n",
       "      <td>-0.423067</td>\n",
       "      <td>-0.424598</td>\n",
       "      <td>-0.074195</td>\n",
       "      <td>-0.352150</td>\n",
       "      <td>-0.074353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.651172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>-0.000079</td>\n",
       "      <td>0.103001</td>\n",
       "      <td>-0.317518</td>\n",
       "      <td>0.062100</td>\n",
       "      <td>0.412106</td>\n",
       "      <td>-0.175753</td>\n",
       "      <td>-0.154370</td>\n",
       "      <td>-0.537214</td>\n",
       "      <td>-0.078601</td>\n",
       "      <td>-0.142166</td>\n",
       "      <td>-0.076855</td>\n",
       "      <td>-0.073155</td>\n",
       "      <td>0.446882</td>\n",
       "      <td>-0.158568</td>\n",
       "      <td>0.222401</td>\n",
       "      <td>0.651172</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    gender  SeniorCitizen   Partner  Dependents  \\\n",
       "gender            1.000000      -0.001874  0.001808    0.010517   \n",
       "SeniorCitizen    -0.001874       1.000000 -0.016479   -0.211185   \n",
       "Partner           0.001808      -0.016479  1.000000   -0.452676   \n",
       "Dependents        0.010517      -0.211185 -0.452676    1.000000   \n",
       "MultipleLines    -0.009451       0.113791 -0.117307   -0.019657   \n",
       "InternetService  -0.000863      -0.032310 -0.000891    0.044590   \n",
       "OnlineSecurity   -0.003429      -0.210897 -0.081850    0.190523   \n",
       "OnlineBackup      0.012230      -0.144828  0.090753    0.062775   \n",
       "DeviceProtection  0.005092      -0.157095 -0.094451    0.156439   \n",
       "TechSupport       0.000985      -0.223770 -0.069072    0.180832   \n",
       "StreamingTV       0.001156      -0.130130 -0.080127    0.140395   \n",
       "StreamingMovies  -0.000191      -0.120802 -0.075779    0.125820   \n",
       "Contract          0.000126      -0.142554 -0.294806    0.243187   \n",
       "PaperlessBilling  0.011754      -0.156530 -0.014877    0.111377   \n",
       "PaymentMethod    -0.005209      -0.093704 -0.133115    0.123844   \n",
       "MonthlyCharges   -0.014569       0.220173 -0.096848   -0.113890   \n",
       "TotalCharges     -0.000079       0.103001 -0.317518    0.062100   \n",
       "\n",
       "                  MultipleLines  InternetService  OnlineSecurity  \\\n",
       "gender                -0.009451        -0.000863       -0.003429   \n",
       "SeniorCitizen          0.113791        -0.032310       -0.210897   \n",
       "Partner               -0.117307        -0.000891       -0.081850   \n",
       "Dependents            -0.019657         0.044590        0.190523   \n",
       "MultipleLines          1.000000         0.186826       -0.066844   \n",
       "InternetService        0.186826         1.000000        0.607788   \n",
       "OnlineSecurity        -0.066844         0.607788        1.000000   \n",
       "OnlineBackup          -0.130619         0.650962        0.621739   \n",
       "DeviceProtection      -0.013069         0.662957        0.749040   \n",
       "TechSupport           -0.066684         0.609795        0.791225   \n",
       "StreamingTV            0.030195         0.712890        0.701976   \n",
       "StreamingMovies        0.028187         0.709020        0.704984   \n",
       "Contract               0.083343         0.099721        0.389978   \n",
       "PaperlessBilling      -0.133255         0.138625        0.334003   \n",
       "PaymentMethod          0.025676         0.008124        0.213800   \n",
       "MonthlyCharges         0.490700        -0.323260       -0.621227   \n",
       "TotalCharges           0.412106        -0.175753       -0.154370   \n",
       "\n",
       "                  OnlineBackup  DeviceProtection  TechSupport  StreamingTV  \\\n",
       "gender                0.012230          0.005092     0.000985     0.001156   \n",
       "SeniorCitizen        -0.144828         -0.157095    -0.223770    -0.130130   \n",
       "Partner               0.090753         -0.094451    -0.069072    -0.080127   \n",
       "Dependents            0.062775          0.156439     0.180832     0.140395   \n",
       "MultipleLines        -0.130619         -0.013069    -0.066684     0.030195   \n",
       "InternetService       0.650962          0.662957     0.609795     0.712890   \n",
       "OnlineSecurity        0.621739          0.749040     0.791225     0.701976   \n",
       "OnlineBackup          1.000000          0.601503     0.617003     0.604117   \n",
       "DeviceProtection      0.601503          1.000000     0.767970     0.763279   \n",
       "TechSupport           0.617003          0.767970     1.000000     0.737578   \n",
       "StreamingTV           0.604117          0.763279     0.737578     1.000000   \n",
       "StreamingMovies       0.606863          0.766821     0.737123     0.809608   \n",
       "Contract              0.035407          0.390216     0.418440     0.327951   \n",
       "PaperlessBilling      0.260715          0.276326     0.310749     0.203907   \n",
       "PaymentMethod         0.003183          0.191746     0.216878     0.117618   \n",
       "MonthlyCharges       -0.710477         -0.513440    -0.597594    -0.423067   \n",
       "TotalCharges         -0.537214         -0.078601    -0.142166    -0.076855   \n",
       "\n",
       "                  StreamingMovies  Contract  PaperlessBilling  PaymentMethod  \\\n",
       "gender                  -0.000191  0.000126          0.011754      -0.005209   \n",
       "SeniorCitizen           -0.120802 -0.142554         -0.156530      -0.093704   \n",
       "Partner                 -0.075779 -0.294806         -0.014877      -0.133115   \n",
       "Dependents               0.125820  0.243187          0.111377       0.123844   \n",
       "MultipleLines            0.028187  0.083343         -0.133255       0.025676   \n",
       "InternetService          0.709020  0.099721          0.138625       0.008124   \n",
       "OnlineSecurity           0.704984  0.389978          0.334003       0.213800   \n",
       "OnlineBackup             0.606863  0.035407          0.260715       0.003183   \n",
       "DeviceProtection         0.766821  0.390216          0.276326       0.191746   \n",
       "TechSupport              0.737123  0.418440          0.310749       0.216878   \n",
       "StreamingTV              0.809608  0.327951          0.203907       0.117618   \n",
       "StreamingMovies          1.000000  0.330993          0.211818       0.123869   \n",
       "Contract                 0.330993  1.000000          0.176733       0.358913   \n",
       "PaperlessBilling         0.211818  0.176733          1.000000       0.101480   \n",
       "PaymentMethod            0.123869  0.358913          0.101480       1.000000   \n",
       "MonthlyCharges          -0.424598 -0.074195         -0.352150      -0.074353   \n",
       "TotalCharges            -0.073155  0.446882         -0.158568       0.222401   \n",
       "\n",
       "                  MonthlyCharges  TotalCharges  \n",
       "gender                 -0.014569     -0.000079  \n",
       "SeniorCitizen           0.220173      0.103001  \n",
       "Partner                -0.096848     -0.317518  \n",
       "Dependents             -0.113890      0.062100  \n",
       "MultipleLines           0.490700      0.412106  \n",
       "InternetService        -0.323260     -0.175753  \n",
       "OnlineSecurity         -0.621227     -0.154370  \n",
       "OnlineBackup           -0.710477     -0.537214  \n",
       "DeviceProtection       -0.513440     -0.078601  \n",
       "TechSupport            -0.597594     -0.142166  \n",
       "StreamingTV            -0.423067     -0.076855  \n",
       "StreamingMovies        -0.424598     -0.073155  \n",
       "Contract               -0.074195      0.446882  \n",
       "PaperlessBilling       -0.352150     -0.158568  \n",
       "PaymentMethod          -0.074353      0.222401  \n",
       "MonthlyCharges          1.000000      0.651172  \n",
       "TotalCharges            0.651172      1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看相关性系数\n",
    "cor = df_drop.corr()\n",
    "cor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0a95066b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OvOuM7waAeQ1H7FrW5sqHPxPl7kO8dzBXF/yMnokh1Ya0U9teZnIaKeGxqldZ3sFUnm1PSbZdoWVtvLYcI+qGD4n+4dz9aj/psYlYN64GgEJXh2YrxuP+8W94/3KCBJ8Q4r2Diz2BKA11p/Tmztf7CVCWo0vvbETP2ICqxZSjulP7EHLmNnc35MTt7oacuNXNG7c3++CjjFvcgyCuKctR7fF52ux9Fwg9e4dE/3DiPAO5tmwHBhYmWDWoorY9qwZVqDutL5fn/FD6AXhCj9ujh0/ZHj1Utkd18rVHtz7+nYBi2qNzr6/D748zxHkGEnvXn6vvbcK0ki3W/8H2+XG99FDWS49vDhB27g6185QvgOyMLFLDY1WvtMh4te/995zj3pf7CC2iT1BWyus4r2m7ZjUcsW1ZG7cPtqjabrcPc9ruqsq2u7z6SdraN0qPT+b0qDU8+vsy8d7BRF57wLVF27BpUqPIH55Lk7Yf1zx/PILHhr+JdHtQaF4q9mhGdkYmbgu2Eu8dTJS7D24Lt1K5f2vMqjk8c3y0ta6B5nMSAJ/tJwm/5EFSQATRtx5ya+1uTJ1tMals98yxKSk5phWuPPtGAFlp6Wr957SYRNV3sfcDODd1PUHHr5PgF0bo+bvcXPsHzj2aodAt35/GyurcJOjkTdzX7eHR4eLPyTKLidvzlDcPKeGxVOzdgrDzd0n0Dy9ymWrDX+Xe1wcI/tedRP9wvH85Qcjpm9Sdrv7jfb0Z/el36UuG+fxMr/+tolK/1sXmperQ9uga6nPl3U3E3g8g8JArd7/+izrT+hRImxoRp5bv7KzyvxnnPys7q/xe/0FyUaAUzZkzh/Pnz3PgwAGOHz/O2bNnuXbtmur7N954g/Pnz7Nz505u3rzJiBEj6N27N15eXqo0SUlJfPbZZ/z666+cOXMGf39/5s2bp/r+888/Z8uWLfz000+cO3eOqKgo9u3bp5aPxYsX8/PPP/P9999z584d3nvvPcaOHcvp06fV0s2fP5/Vq1dz7949XFxcyigqoqRMq9hh7GBNyOlbqs+y0jIIv+hBhZa1i1yuQstahJxWHxoXcuomtq3qAKCjr4u1S3W19QKEnL6FbTHrzU/XQA+ys8lKS8/NX2o6WZlZ2LauW+L1PK2n2Y8KLWsXTH/qJjZNqqPQ01WmKT5+JRV+xRPnns0xdrQGwL59A8xrOBJy6tmHLT4tRRExC9UQs9BCYmadJ2YvOrMqdhg7WBGcr66FXvLArpg6YduiltoyAMGnbqqW0dHXxcalOsGn1U9ig0/fVsVb1yBn1r7M1Nx6lJ2VTVZ6Jnb5yly1oe0Zdvt7+p1cQ7Olo4u9U+hZlGfbU9JtR1y5T5WBbTGwMgWFgsqD2qJjqE/YhXtAzhQLJhVtICubnsdWMvDGBjrumI9FHeenjIpmReU9rATlKH/cgk/dxLZlbtxsnrCt09HXpdbYLqTFJhJ9N3eKBV1jA9p/Nwu3RdtICY994n0sTQp9XaxcqhdoX0JP3yqynNkU0h6FlkJ7pG9uAkBadMJTr+NZlVX7XKFlLUILqZcV8rUvZjUc6H99A30vf0mb72dhWuX5/ZBWlPI6zpdku7oG+kBhbXeGqg9UHv0kbe8b5advYUx2VhZpsUmaEz+DF+G4pomugX7Oj8J5bv7KTEkDeObypM117WnoGhtSfVQnEvzCSA6KfOr1PAk5phVOG/pG9u3qM+Tmd/Q7+xmtPp2CYQWLYvOsb2FCekIy2Znl94NfWZ2bPAmHdvUZdvNbBpz9lDafTtYYt+fB0NaCit2a4vP76WLT6RjokZmapvZZZnIadnnaysYfjqD6qI64fbiFI53nc/+HI7Td8BZ27eoVuV7blrUJv+ihdoEu5NRNTJxsMM13AbLn8Zzzj85/LMC+/fO5YVKI0iDPFCgl8fHxbNu2jd9++41u3XLuCP3555+pWLEiAN7e3vz+++8EBASoPps3bx5Hjhzh559/ZtWqVQCkp6ezceNGatbMGfI1a9YsVqxYodrOV199xYIFCxg2LGcu2I0bN3L06FHV94mJiXzxxRf8+++/tGuXc+dSjRo1OHfuHJs2baJTp06qtCtWrKBHjx5F7lNqaiqpqepzVuqkpmJoqB1DE/9rjOytAAr8cJMSEYtJJduil7OzIiVcfa7alPA4jOwsATCwMUdHT7fgesNjVWlKIvLaAzKSUmmyeBQ3V/8BKGiyeBQ6ujoYO1iVeD1P62n2w9jOkpBC0uvo62FoY05KWIzG+JXU9cXbaPnZFAZe30BWegbZWdlcnbeZiCueT7Se0mSojFnqE8TMyM6yQIxT88XsRVdkXQuPxVRTXYsoWD8fx/JxvAukCY/FWLnN2AfBJDwKp+mCkVz54CcyklKpN60vxg5WavXId+95Eh+FkxwWi1W9SjRd8BrWDarw76i1T7nXRSvPtqek27447RvabZrNkHs/kJWeQUZyGucnfUmiXxiQM483QMN5w7ixbDuJj8KpO60fXfct4dArc8vkbifjZypH+eIWEVeicmRkr15vK3ZvRvvvZ6FnbEByaAwnR60hLSr3R4Hmy8YS4epJ4NHyf55HUe1Rqob2qLD0z9oeNVk2hojLHsTdD3iq5UtDWbXPJTmmRV335srbG4n3DsHIzoL67w6m69/LONr5g3L9Uam8jvMl2W7cgyASH4XjsnAkrvN/IjMplTrT+mLsYI2Rsu0uj36StveN8tIx1Mdl0Sj89l0gI6Fs5+5+EY5rmoSeu0PTZWOo+1Y/vDYfQdfEkMYLXgN45vKkzXXtSdSa0B2XJaPRNzUiziuQUyNXk5We+cTreRpyTCtcefeNgk+68+jgZRIDIjCtYofL/BF03b2Qo70XFzrywsDajEbvDuHBr/8+0X6WtrI6Nymp4JPu+B+8QmJABGZV7HCZP5zuuxdwuPeSIkesPA/VX+tIekIKAYeuFpsu5NQt6k7rS/glDxIehuHQoSHOvVug0Mm5B1rX2JA6b/bl1IiVqtFZif7h2LWuQ82x3Qi/6FHoeo3srUh8pD5C4fH/kZG9JYmPwkkJi+HqvM1E3/RFx0CPasNfpfPuBZwctpLwS4WvVzyjrOfTzr8s5KJAKfHx8SE9PZ3WrXOHIFlaWlK3bs7VyWvXrpGdnU2dOup32KSmplKhQu4QWhMTE9UFAQAnJyfCwnJ+9IiNjSU4OFj1Yz+Anp4eLVu2VE0hdPfuXVJSUgr82J+WlkazZs3UPmvZsmWx+7R69WqWL1+u9tni999m6fx3il1OlEzVoe1psW6y6v3Zx/P25R9pplBongs63/cKRcHPCrx/wvltUyPjufDm17Rc8wa1J/ciOysb//0Xibrp+3zvrHjS/SiQXFFwPSWJnwa1J/eiQvNanB3/GYkBEdi1rUeL1RNJCY0m9OydJ1pXacs/xRgKRYFipr5AvvfKmBVYzwui2pD2tF43SfX+1LjPgMKKkqLgvudTMASF1M8CK86NXXZGJmenrKfNF1MZce8HsjIyCTl7h8ATN9QW8f7tlOrv2PsBxPuE0OfoJ1g3rkb0rYfFZ1IDrWx7NGy78QcjMLA05eSIVaRFxePcuyXtf3ibfwd/TKzHI1Wn/+76/QT8k3PicOW9TQy49g2VB7TBuxRO9qoOaU+rPHE7rYxbgRA9RdwK+6yweps/TqHn73Kkx0IMbcypOaYLr2yazbF+H5EaGYdzz+Y4vNKQIz0XFp+X5+xJ26NC41vYekqo6aqJWDaowqlBKzQnfg7KpH3WUC9D/nVX/R3nAZGuD+h76QuqvtYBr02HS575slJex/litpudkcn5KV/R6vM3GerxI1kZmYSevU1Qnra7XPtJWto3Ui2rp0u7jbNQ6Chw+3DrU62jOC/icU2TOM9ALr+ziabLxuCycCTZmVl4/XSU5LCY0itPWljXnoTf3vOEnLmFsYM1daf3pf0Pb3Ni4HKy8ozoKWsv+zFN2/pG/gcuqf6OvR9AlLsvA6+sp2K3pgTkmz5Hz8yYTr+8T6xnILe/2Ft83krZcz830cDvwGXV37H3A4h092Xwla9w7tZU47RDzyJ/231mzDq1B3VXH90J/73nNdbp60t/oeVnU+hz9jPIzibhYSi+O89QfVTOw5Mt6zijZ2xAp10L1JbT0dcj5vZDAHqfWqu6cBtx+T5nxiifRVGgTXvc7uX8E+8dTHyeh3xHuj3AuGIF6k7vKxcFxAtBLgqUkscHKEW+Ts3jz7OystDV1cXNzQ1dXfXhgWZmZqq/9fX11b5TKBRP1EnIysrpJP7zzz84O6tPm5D/Dn9TU9Ni17VgwQLmzJmj9plOfGCJ8yKKF3j0GpHXvFXvdZTTihjZW6rdKWJUwaLYaR9SwmMK3D1qaGuhutsiLSqerIxM1R0IqvXaFr/ewoSevsU/7eZgYGNGdkYW6XFJDHT/loRi5vgrLU+zH8mF3FlraGtBVnoGqcq7ITXFryR0jfRpvGAk5yd9SbDyB4LYe4+wbliVum/1K7eLAqnFxCz/nUqPFXY38uOYacOw5KcRcOwaEddz69rjKXyM89U1Qw11IiU8BuN8d94Y5SkrqnjbWeVLo353b9SthxzusQh9c2N09PVIjYqn18FlRN30LXLbUbcekpmWgUV1h2e+KKBNbc/j7RW3bdOq9tSe3IvDneYT55lzDIq5649tm7rUeqMHbh9sISU0Z9nH30POsOtEv7BSm7s68Ng1Iq8XjFv+cqSpbU0JjylwB1dh5ci4QDkquN7M5FQSHoaS8DCUyGsP6H/uc2qO7szdDQdweKUBZtXsGebxo9oyr/74LuGXPfh3+MoS73tpKKo9MnyO7VHTT8ZTsWdzTg35mOTgqCdevjSVVfv8NMe0zORUYu89wry641PsSekpr+N8SbcbffMhx3oszGm7DfRIjYyn+z/LiXLPbbufdz9Jm/tGjyn0dGn/w2zMKttxcsSqMhkl8KId10rKf98F/PddwNDWgsykVLKzoc60viT4hz3RevLT9rpWUunxyaTHJ5PgG0qkmxdDPH6gUp+W+O+/+MTrelJyTMuhjX0jtfWGxZAUEIF5DfXjm56pEZ1/m09GUgpnJ39JdsbzvfP4eZ2bPK2UsBgSC4lbacvfdieH5JZj2zZ1sahVkYvTvtG4ntTIeM6/8SU6hvoYWpuRHBKNy6JRuc8hUD6A/Oy4T0kOjlZbNlM55d+ZsZ+io5zG6/FUbY9HQOVlaJszrVJx/y+R1x5Qbdh/7/mXWuM/Ord/eZFnCpSSmjVroq+vz5UruQ81jIuLUz0voFmzZmRmZhIWFkatWrXUXo6OJWtsLS0tcXJy4tKl3CvgGRkZuLnlTgvQoEEDDA0N8ff3L7CdypUrP9E+GRoaYmFhofaSqYNKT0ZiiurHnISHocR5BpIcGo1jx8aqNDr6uti1q0ekq1eR64l0fYBDnmUAHDu5EHE1Z9qarPRMom/64tixkVoah46NiShmvcVJi0ogPS4J+1caYGRrQdCxa5oXekZPsx+Rrl6FxKYxUe6+qs6fpviVhEJPD10DvYIP/c7KUt3BXB6ylTFzKIWYReeJ2Ysmf12L9QwkOTQGpzxx0dHXxaFtPcKLqRMRbg8KlD+nTo1Vy2SlZxJ101dtvQBOHRsVGu/0+GRSo+Ixr+6ATZMaPCpmihfLupXQNdAjWfnj97PQprYn0T9c47b1jHOOO4XXr5xOftRNXzJT0jCv6aT6XqGni2llOxIDIkoWGA1KGjf7EpWjQuLmmhu3qELi5liSNlsBOoY5J5Z3N/zN4W4LONJjoeoFcH3Zdi6/9/wfOpydnklMEe1RUeUsqpD2yOEp26OmKyfg3LcVZ0asJOlR2V/I1qSs2ufC6qVDJxciizmm6RjoYV7buVTal2dRXsf5J91uenwyqZHxmFV3wLpJjUKn53pe/SRt7htB7gUB8+qOnBq5usxuLnjRjmtPKjUijoykVKoMaktWatozPwD8RalrT0yhQMdAX3O6UiDHtBza3jcysDbDpKKN2vFNz8yYLr9/SFZaBmcmfv5cR5Y89rzOTZ6WgbUZpvniVhbyxyEzJff/osbozkS5+xBz17/E68tKTSc5JBqFni6V+rVSHZ/jPAPJTEnDxNlWbXsJD0NJDsq5EJEUEJH7WUjOhYMIVy/s2tZDRz/3pl7HTo1JCo4qMK1QXtaNqpV7n0qIkpKRAqXE3NycCRMm8P7772NjY4O9vT0fffQROjo6KBQK6tSpw5gxYxg/fjyff/45zZo1IyIign///ZfGjRvTt29fzRsB3nnnHdasWUPt2rWpX78+X3zxBTExMWr5mDdvHu+99x5ZWVm8+uqrxMXFceHCBczMzJgwYUIZReD5SUpKxj8gSPU+MCgUD09vLC3McXK0L8ecPTvPH49Q/+2BxPuGkOATQv23B5GZnIbf3guqNG2+nk5SSDS3Vu3KWWbzEbruW0K9mf0JPOqGc68WOHRoyIk8w0jvbzpMm2/eIsrdlwg3L2qO7YqJcwW8fzmhSmNgZYqJs61qntLHP66lhMWoroRXH9mROK8gUiLjsG1Zm2YrxuH5wxG1IXNlSdN+NF44EhNHay6/vREA719OUHtSD5ouG4P3jpPYtqhN9dGduTRjg2qdJYmfnokhZnnuoDStYodVw6qkxSSQFBhJRkIyYRfu0nTJaNyS00gKiMCuXX2qDu/AjWXbn0tsiuKpjFm0uy+Rbl7UUMbMRxmzRgtHYuxozdU8Mas1qQdNlo3BZ8dJKhQSM4W+LhZ1KgE5wy6NHa2xbFiVjMQUEh+GAqBbSMwslTFLDnw+D4ErjsfmIzScPZA4n1DifUNo9PZAMpLTeLgvt661Wz+N5JBobqz+Q7nMUXrsXUyDmf0JOOpGpV4tcOzQkGODP85d7w+Haff1W0Te9CHC9QG1xnbBxLkCXnnqWpX+rUmJjCcpMAKr+pVpsWIcAUdcCVE+oNisqj3VhrYn6IQ7qVHxWNZxpvlHrxN16yHhT/iDTEmVZ9ujadtxD4KI9wmh5brJuC/fQWp0ApV6t8SxYyPOKodbZyQk4/3rCRrNG05SUBRJARHUe6sfAI/+zh0SXdrubz5Cg9kDifcJId43hAZvDyIjOQ2/POWo7frpJIdE4746N27d9i6hfr5y9L/BeeL2w2Hafv0WUTd9iXDNjdvjcqRrbEjDdwYReOwayaExGNqYUXtCd0ycbPBX7m9KeGyhdzElBkYUeyJTljw3Hab1E7ZHNSf1wGXZGHzztEeXn7A9arZ6IpWHtOfCG1+QnpCCofKuuvT4JLJSnv+PAY+VRfvstfkInfctoe7M/gQddaOisl6ezFMvXZa+TtDxayQFRGJkm/NMAX1zY/x2n1Wl0S+mT1DUXbClobyO8yVpqyr1b02qsu22rF+F5h+PI/CIq9qDQ8ujn6StfSOFrg6v/PgO1o2rcXb8Zyh0dFR3AqfFJJT53O/afFyDnPnljeytMK/uAIBl/cpkJKSQFBiheg5OrTd6EOnqRXpiCo4dG9Nk6WhurtxFetyzP6hZm+uapnMS0yp2VBnUjpDTN0mNjMfY0Zp6swaQmZymGrH7PMgxrXDl1TfSMzGk0bxhPPrnCimhMZhWtsNlwWukRiWopg7SMzWiy+8fomdswMXZ36FvZoy+mTEAqZFxZGeV31SpZXVuomdiqGpnAMwq22HdsAqpMYkkBUaiZ2JI43lDefTPVZKVcWu6YASpUQllOnVQcfTMjKk8oDU3lv9W6Pf5226bZjUxdrIm5rYfxk42NJo7FIWODh7fHgRyLj54bDxEs+VjUSgUhF+5j765MbYt65CRmMLDPP2fvPz3XaDh3KG0/mo6977+C7MajtR/exB3v9inSlNnam8SH4UTez8AHX09qg5/lcr9W3Nu8pelHBUhyoZcFChFX3zxBdOnT6d///5YWFgwf/58Hj16hJGREZDz4OFPPvmEuXPnEhgYSIUKFWjXrl2JLwgAzJ07l+DgYCZOnIiOjg6TJk1iyJAhxMbmnqB9/PHH2Nvbs3r1anx8fLCysqJ58+YsXKhd8wo/rdseXkya/YHq/bpvcu54HNSnOysXzy2vbJUKj28PomtkQIvVEzGwNCXyujenR60hIzFFlcbEuYJahyXS1YuL0zfQ+MMRNJo/gkS/UC5M/4aoPEMSHx24hKG1GQ3nDMHI3orY+wGcHfspSXnuoK3YswVt1k9TvW+/aTYAtz/7kzuf58yzaF7TicYLR2JgZUbSo3Dufv0Xns9x/mFN+2Fsb6U2VUjio3DOjP2UZsvHUmtiD5JDo7m+5BfVnONQsvhZN6lB172LVe+bLR8HgO+uM1x5dxMAF6dvwGXhSNp+OyMnPoER3Fr7h9pJTnkIUMasgTJmcRpilvQonLNjP6Xp8rHUnNiDFGXMAvPEzNjBmp7/W6V6X3dGf+rO6E/YhbucHpYzJYlNkxp0zhOzpsqYPdx1hqvKmJWnu8q61nr1RAwsTYi47s2/o9eq1TVTZ1u1uhbh6sW5tzbQ5IMRuLw/nAS/UM5N36A2bNrvwGUMrM1p/N4QjO2tiLkfwKmxn5KY50KIsYMVzZeNyZlWKCwGn93nuP1VbucyKz0Dx1cbUm9yL/RMjUgKiiLwxA1ufbG3zE5WyrPt0bTt7IxMzoxdh8uiUXT4ZR56poYk+IZy+Z1NBOeZG/3Git/Jysii7TdvoWtkQOS1B5wcvpL02Gf/8aQo95R5b5kn76dGFx+3CFcvLry1AZcPRtD4/REk+IVyfvo3auXI/8AlDKzNaKgsR7H3Azg99lOSAnPilp2VhUWtilQf0QFDG3NSoxOIcvfhf0M+VptCSdsEKPerfp726Fye8mBUSHt0buynNMnTHt0opD3qUUh7FJ6nPao5Mec5S533LlHLz9V3NuH3x5ky219NyqJ9jnT14tL0DTRS1ssEv1Au5auXxk42tP1uVk7ZiYwj8toDTvT/qECfoHWePkE7ZZ/gzmd/cvfzspt7ubyO8yVpq4wdrGm2bCyGdjlt98PdZ7n7ZW7bDeXTT9LWvpGxkw3OvVsA0OvEarU8/zv0E8Iv3iv9YOShzcc1gJrju9Fo3jDV+277lwJw+Z1NPFS2SxWa1aTRvGHomRoR/yAI1/lb8NtzrlTio811TdM5SWZqOrZt6lJnam/0LU1JDY8l/LIHJwYuJzXy2aZNeRJyTCtcefaNrOpVpvrwV9G3MCUlLIbQ83e5MP0b1bZtXKpj26IWAAMuqv9oe6D1O6U2uvRplNW5iU2TGvT4c5HqfYvlYwHw3nWGS+/9oIpbjTxxCzl/l3PTN6ht+3mqMrgtKBT457kgklf+8qNrpE/jD17DrIodGUmpBJ+4waXZ36tdQL29djepEXHUf3sgLavYkx6XSPSth9z7+kCR+UiPT+b0yDU0Xz2RHkc+Ji02Ec9Nh7m/6ZAqjY6+Hk2Wvo6xow2ZKWnEeQZwZsw6tXMUUcqyZPqg0qTIflGfHPkCSExMxNnZmc8//5zJkydrXuAFkB7hU95Z0Hp7Gy/RnEgIDWRuN83SnvLBdC8TPTnEFytLipBG+lKENJIQaSanb0IIbaGrOclLL136RxplIUHSRM5DNBsZvKO8s/DCSfO5ojlRGTGo0brctl1WZKRAKbp+/ToeHh60bt2a2NhYVqzIGe42aNCgcs6ZEEIIIYQQQgghhBBCvJiy5UHDpUouCpSyzz77jPv372NgYECLFi04e/Ystra25Z0tIYQQQgghhBBCCCGEEEIuCpSmZs2a4ebmVt7ZEEIIIYQQQgghhBBCCCEKJRcFhBBCCCGEEEIIIYQQQmgvedBwqZJnWQohhBBCCCGEEEIIIYQQLwkZKSCEEEIIIYQQQgghhBBCe8mDhkuVjBQQQgghhBBCCCGEEEIIIV4SMlJACCGEEEIIIYQQQgghhPbKyizvHPynyEgBIYQQQgghhBBCCCGEEOIlIRcFhBBCCCGEEEIIIYQQQoiXhEwfJIQQQgghhBBCCCGEEEJ7yYOGS5WMFBBCCCGEEEIIIYQQQgghXhIyUkA8kb2Nl5R3FrTe0Fsfl3cWtN4ul6XlnQWtd80go7yzoPWGpUiMNPHSNSrvLGi1m/pShjSxztYt7yxoPXncmWbRComSJo7ZclqmibRGmqWSXd5Z0HrSGmlmna0o7yxovT6mEeWdBa0XEm1e3lkQ/0VZMlKgNMlIASGEEEIIIYQQQgghhBDiJSEXBYQQQgghhBBCCCGEEEKIl4SMUxVCCCGEEEIIIYQQQgihveRBw6VKRgoIIYQQQgghhBBCCCGEEC8JGSkghBBCCCGEEEIIIYQQQnvJg4ZLlYwUEEIIIYQQQgghhBBCCCFeEjJSQAghhBBCCCGEEEIIIYTWys7OLO8s/KfISAEhhBBCCCGEEEIIIYQQ4iUhFwWEEEIIIYQQQgghhBBCiJeETB8khBBCCCGEEEIIIYQQQntly4OGS5OMFBBCCCGEEEIIIYQQQgghXhJyUQDo3Lkz77777nPbXrVq1fjqq6+KTbNs2TKaNm36XPIjhBBCCCGEEEIIIYQQWisrq/xe/0FaP31QWFgYS5Ys4fDhw4SGhmJtbU2TJk1YtmwZ7dq1K5Vt7N27F319/VJZV1xcHGvXruXPP//k4cOHWFlZ0ahRI2bMmMGQIUNQKBRcvXoVU1NT1TIKhYJ9+/YxePBg1Wfz5s1j9uzZpZInbdJw7lBqju2KvqUpUdcf4LZgK3GegcUuU6lfKxrNH4FZVXsS/MK4teYPAg+7qqWpNaE7dWf0w9jeiljPQK4v/ZWIy/dV3zv3bUnNcd2wcamOoY05R7svJOaOn9o6TKva0/Sj17FtXRddA32CT7pzbdE2UiPiSi8Az4nrjVv8/Nse7no8IDwyivWrl9CtY/vyzlaZcZk7lFpjumBgaUrkdW+uLNxKrIZyVblvK5rMH455VXvi/cJwX7ObR0dyy5V9m7o0mNEPm8bVMXG05tSkLwk44lZgPRa1KtJ88Sjs29ZDoaMg5n4gZ6d/Q1JgZKnvZ2nr9e5w2o7uiomlGX43HvDnki2EegUUmb7tqK60HNoRx7qVAAi45cuhT3fi7+6tSlOjdT26vDmASo2rY+lgw5Y3P+P2MdeiVqm1HCf2wnnGQAzsrUm6/wjfpVuJu3yv0LT69lZUXzYBM5caGNVwInjzIXyXblVLY1y3ElXeH4VZkxoYVbbHZ8nPBP/4z3PYk9LVdM5Q6ijrWsR1by4t2kqMhrpWtW8rmr2fW9eurd2Nf566Vnd8N+qO64ZZZTsAYjwDcP9yH4EnbwKg0NOl+fzhVOraFLOqdqTHJRN07jZuq3aRHBpTZvtamqSuadbx3aE0f70rRpamBF5/wJElWwn3Krps2dV2ptPc4Tg1qo5VZTuOLv+VK1uOFJn+lRkD6frBSC7/dJhjK7aXxS6Uuc7vDqVFnhj9U4IYdZk7nIrKGB1Z/iuXComRuYM1PRaMolbnJugbGRDpE8Jf838g+PbDMtyb0tf73eG0H90VY2U927NkCyHF1LN2o7rSamhHnJT17NEtXw7mq2fdZwyiSa/W2NesSHpKGr7XPPl7zW+E+QSX+f6UhVffHUrT17tgZGlK0HVvji3ZSkQxZci2tjMd5g7DUVmG/rf8V65uOaqWptnYbjQf2w3LSjlteIRXAOfW78Pn1M0y3Zey8sq7Q2nyehcMLU0Jvu7N8SVbiSwmRhVqO/OqMkaWle04sfxX3PLFqM2MAdTp3YoKNZ1IT0kjyM2L02t2EfWCliNprzWT9rqgNu8NpZGy/Qm57s3JJVuJ0tCHrNWnFW3nDceyij2x/mFcXLcb76O5fR19UyPazRtOzV4tMbG1IOz2Q84s207oTZ9C19d19SQaj+nK6eW/cuOno4Wm0RaWo/tjM2k4unY2pD3wI3z1RpLd7hSa1qzHK1iO6odhvRooDPRJe+BP5IbtJJ3PPXe1HNEb84HdMaxdFYCUuw+I/PJnUm55Ppf9eR6cJvak8oxBGNhbkXg/AO+lPxN32aPQtAb2VtRQnr8Z13AkcPNhfPKdvwnxMtD6kQLDhg3D3d2dbdu24enpyYEDB+jcuTNRUVGltg0bGxvMzc2fevnMzEyysrKIiYmhffv2/PLLLyxYsIBr165x5swZRo4cyfz584mNjQXAzs4OExOTYtdpZmZGhQoVnjpP2qjezP7UndYXt0Vb+V+fJaSExdJ51wL0TI2KXKZCi1q02zgbvz3nONp9AX57ztF+02xsmtVUpak8sC1NV4zj3vq/ONpzERGXPei4Yz4mzrnx0zMxIuKKJzdX7ix0O7rGhnTe+SFkw6nhqzgxcDk6Bnp0+GUeKBSlF4TnJDk5hbq1arBwzozyzkqZazCzP/Xe7MPVRds43HcpyeExdNv5YbHlyrZFLTpsnIXvnnP802MhvnvO0WHTLCrkKVd6JoZE3/Hn6qJtRa7HrKo9vfYvIfZBEMeHr+Sf7gu5/dV+MlPSS3Ufy0LX6QPpNLkve5f+zJcDFxIfHsP07QsxLCZuNds24NqB83w3+mO+HrqU6KAIpv26EEsHa1UaAxMjgu75sXfpz89jN8qE7aD2VF8xkYCv9nKjx/vEXb5Hg98WYuBsW2h6HUN90iPjeLR+L4n5LjY+pmtsSKp/KH6f7CAtNLoss19mGs3oT4M3+3Bp8TYO9supaz1/L76u2bWoRafvZ+H95zkO9FiI95/n6LxxFrZ56lpicBRuq3dxsO8SDvZdQvD5u3TdMgerOs4A6BkbUKFxNdzX7+fv3ks4OfUrLGs40e3nOWW+z6VB6ppm7af3p+2UvhxZupWfBiwhMTyWMTsWYFBMjPSMDYn2D+PftTuJDyu+Tjm51KDZ610IvVt4/XwRvDK9P+2m9OXQ0q38OGAJCeGxjNcQI31ljP5XTIyMLEyY/OdHZKZnsmPCOr7tPp+jK3eQEpdUVrtSJrpNH0iXyX3Zs/RnvlDWsxka6lktZT3bMPpjvlTWs7fy1bNabepz9tdjfDlkCd+NW4muri5v/bIQA2PD57Fbpart9P60ntKHY0u3sXXAUhLDYxi140ONZSjGP5xTa3eREBZTaJr44ChOrd3F1gFL2DpgCQ8v3GX4j3Owre1cRntSdlpP70/LKX04vnQbvypjNLIEMYr1D+d0MTGq3KY+1385zq+Dl/HH2LXo6Oky4tcP0H8By5G015pJe11Qi7f602xKH04t2cbO/jl1a8iOD9EvJiaOzWvR59tZeOw9x2+9F+Kx9xx9vpuFQ9PcPmT3dVOo0qERR9/9nu09FuB/9jZDfvsQ0zzt+GM1erbAsWlNEkJK73eksmLWpyP2H04jctNO/IfOJNntNs6bPkHPya7Q9MYtG5F04RqB05biP3w2SZfdcf5uGYb1c2Nl3MqF+EOnCJj4Af6j3yMjKAznzavQs/9v/OZkN6g9NVe8gf9Xf+LWYz6xl+/R+LdFGBZx/qZQnr/5r/+zyPM3IV4GWn1RICYmhnPnzrF27Vq6dOlC1apVad26NQsWLKBfv34AxMbG8uabb2Jvb4+FhQVdu3bF3d1dtY7H0/D8+uuvVKtWDUtLS0aNGkV8fLwqTf7pg6Kjoxk/fjzW1taYmJjQp08fvLy8VN9v3boVKysrDh48SIMGDTA0NMTPz4+FCxfy8OFDLl++zIQJE2jQoAF16tRh6tSp3LhxAzMzM0B9+qBq1aoBqEYRPH6ff/oghUJR4PU4LcDdu3fp27cvZmZmODg4MG7cOCIiItT28e2332b+/PnY2Njg6OjIsmXLnuF/58nVmdqbu+v3E3jIldj7AVx+ZyO6xgZUHVr0Hex1pvYh9Mxt7n1zgPgHwdz75gCh5+5QZ2pvVZq60/rg+/spfH47RbxXENeXbic5KJKaE7qr0vjtOcfdL/cRcuZ2oduxbV0Hk8p2XH5nE7Eej4j1eMSVdzdRoVlNHF5tUHpBeE46tGvF229OoEfnV8o7K2Wu/pTe3P76Lx4dzilXF97ZhJ6xAdWHFF2u6k3tTfCZ29zZ8DdxD4K5s+FvQs7dpX6echV08ibu6/bw6HDRd942/XAEgf+6c/2TnUTf9iPBP5zAEzdIjdT+0SUdJ/Xhf9/u59bRq4R4BvDb3O8wMDak+aCiy8yOdzdwYftxgu76EeYdxB8f/oBCoaD2K41UaTxO3eDw539w6+jV57EbZaLitAGE/v4vob+dINkrEN+lW0kNjMRpQs9C06c+Csd3yc+E7z5NZnzhJ2YJN7x5uOJXIv46T1aa9l80KkyDKb25+fVf+B92JeZ+AGffzalrNYqpaw2m9CbozG1ubfibWO9gbm34m+Bzd2kwJbeuBRy/TuC/7sT5hBDnE8L1tbvJSEzBrnktANLjkzk2ei0P/75MnHcw4de8ubT4F2yb1MC0ovafyEhd06z15N6c27AfjyOuhHsG8NfcjegbGdBoUNFlK/imDydW/c6dvy+RmZpRZDp9E0OGrJ/BPx9sJjk2sSyy/1y0ndybMxv2c++IK2GeAexTxqhxMTEKuunD8VW/c7uYGL361gBigyP56/0fCHT3ISYgAt/zd4j2DyurXSkTnSb14di3+7l59CrBngFsn/sd+saGtCimnv367gbObT9OoLKe7fzwB3QUCurkqWcbJ6zhyp7ThHgFEHTPnx3vf49NJTsqN67+PHarVLWa3JsLG/7C84grEZ4BHJy7CX0jAxpoqGcnV/3Ovb8vkZFa+LHrwYnreJ90J8o3hCjfEM58upu0pBQqKtvwF0nLyb25uOEvvJQxOjR3E3pGBtQvJkYhN304tep3PP6+RGYRMdozYR2395wl0iuQ8Hv+HJr3A5aVbHFoXK2M9qTsSHutmbTXBTWb3JurG/7C+4grkZ4BHJ+T0/7UHVx0TJpN7o3/2du4fvs30d7BuH77N4/O36XZ5Jw+pK6hPrX6tOLcqp0EXblPrF8ol7/cS9yjcFzGdVNbl6mDNZ0/nsCRd74jKz2zTPe1NFhPGErs3qPE7TlCms8jwldvIj0kHKtR/QtNH756E9E/7SH1tifpfkFEfrWVNP8gTLu0UaUJmb+O2N8PkurhQ7pvAKFL14OOAuN2TZ/TXpUt52n9Cfn9X0J++5dkr0B8lm4lNTCi2PM37yU/E7b7DBlFnL8JLZWdVX6v/yCtvihgZmaGmZkZ+/fvJzU1tcD32dnZ9OvXj5CQEA4dOoSbmxvNmzenW7duaiMJvL292b9/PwcPHuTgwYOcPn2aNWvWFLndiRMn4urqyoEDB7h48SLZ2dn07duX9PTcjl5SUhKrV69m8+bN3LlzB3t7e3bu3MmYMWOoWLFiofuip1dwtqarV3NO5n/++WeCg4NV7/MLDg5WvR48eECtWrXo2LGj6rtOnTrRtGlTXF1dOXLkCKGhobz22mtq69i2bRumpqZcvnyZdevWsWLFCo4fP15kHEqTaRU7jB2sCTl9S/VZVloG4Rc9qNCydpHLVWhZi5DT6sOPQ07dxLZVHQB09HWxdqmutl6AkNO3sC1mvfnpGuhBdrbaj3VZqelkZWZh27puidcjni+zKnYYO1gRnK9chV7yKPb/365FLbVlAIJO3XyiMoNCgXO3psT7hND1t/kMv/ktvQ8uo1LvFk+8H8+bTWV7LOytuX82t25lpmXgffke1VrUKfF6DIwN0dXXIynmxT1xy0+hr4eZSw1iTrmrfR5z2h3zVi9vW2BWxQ4TByuC8tW1kEse2Guoa0Fn1Ota4OmbRS6j0FFQfWBb9EwMCXPzKjQNgIGFMdlZWaRp+d1xUtc0s6psh7m9NT5nc8tJZloGfpc9qNTiCdrkIvT5eCJe/97A93zhQ+5fBNbKGHnni9HDyx5UfsYY1e3RgqCbvoz47m3ed/uOaYdW0nxUl2fN8nNVobI9lvbWeBRSz6o/YT3T0VDPjM1zRvomxSQ8fYbLgVVlO8zsrfDNV4b8S6mePabQUVB/QFv0jQ0JvFZ0G66NLJUxepgvRo8ue+BcijECMFSWo5QXrE2X9lozaa8Lsqhih6m9Ff5n1GMScNkDp2Ji4tS8ltoyAP6nb6qW0dHTRUdPt8DFuIyUNCrm7bMrFPT6ajrXNv2jcboiraCvh1HD2iSdv6b2cdL5axg1q1+ydSgU6JgYkxUTX3QSI0MUenpkxRad5kWh0NfD3KUG0fnO36JP38TiJT5/E6IktPqZAnp6emzdupWpU6eyceNGmjdvTqdOnRg1ahQuLi6cPHmSW7duERYWhqFhzvDLzz77jP3797Nnzx7efPNNALKysti6datqiqBx48Zx4sQJVq5cWWCbXl5eHDhwgPPnz9O+fc6V6x07dlC5cmX279/PiBEjAEhPT+e7776jSZMmQM6zD6Kjo6lXr94T7aOdXc4QMCsrKxwdHYtM9/i77Oxshg0bhqWlJZs2bQLg+++/p3nz5qxatUqVfsuWLVSuXBlPT0/q1Mk5IXJxceGjjz4CoHbt2mzYsIETJ07Qo0ePJ8rz0zCytwIgJTxW7fOUiFhMKhU+pAvAyM6KlHD1u65TwuMwsrMEwMDGHB093YLrDY9VpSmJyGsPyEhKpcniUdxc/QegoMniUejo6mDsYFXi9Yjnq8hyFR6LqaZyFVGwLBo/QZkxsrVA38yYhrP6c2PtHq6v3EnFLk3otPkdjg9fRdilwucv1AYWdlYAxOeLW3x4LNbFxC2/fh+MJjYkCs/ztzQnfkHo25ij0NMlPV9s0sNjMVDG7WVkrKxryfnqTXJ4LGbFlBljOyuSwwsuk7+uWdWrRL8Dy9A11CcjMYV/p3xFrFdQoevUNdSnxYKR+Oy7SHpC8pPvzHMkdU0zM2XZSsgXo8SIWCyLGPJdUg0HtMWpUXU2D1zyTOspb49jlFgGMbKubEersd24uPkwZ7/9C+cmNemzfDyZaem47z33TOt+XsxLqZ4NUNaz+8XUs8GLx+F9xYNgz6KfVaCNTMuwDAHY1a3E+H3L0DPUJy0xhb3TviKyiDZcWz2OUVK+GCVFxGJRCjHKq+uSMTy6cp+IF6wcSXutmbTXBZkq2+ikiCerWyZ2VoUuY6LsQ6YnphDk6knrtwcT9SCQpPBY6gxqj2OzmsT4hqqWaTmjP1mZWdzYot3PEHhM18oChZ4uGRHq00hlRkajZ2tTonVYvzEMHRMj4o+cKTKN3dxJZIRGknTh+jPlVxvknr/FqH2eFh6D9Ut8/vaflaX9o31eJFp9UQByninQr18/zp49y8WLFzly5Ajr1q1j8+bNhIeHk5CQUGDu/eTkZLy9cx8SVq1aNbVnBjg5OREWVvgwu3v37qGnp0ebNrlDrSpUqEDdunW5dy/3IZMGBga4uLio3mdnZwM50/yUpYULF3Lx4kWuXr2KsbExAG5ubpw8eVI1PVFe3t7eahcF8iouDgCpqakFRmikZ2eir9DVmM+qQ9vTYt1k1fuz4z7N+SM7X0KFArLzf5hPvu8VioKfFXj/hP8PqZHxXHjza1queYPak3uRnZWN//6LRN30JTvzvzlM6EVUbUh72qybpHp/ctxnOX8UWq40rKzA9yUoi3lT6+SUsUdHr+HxY86DwKLv+GPXsjZ1xnfTqosCzQe9wohVU1XvN09aC+S2W48VWreK0GXaAJoPfIVvR60ockqBF1qBNuWJiscLr8aQ9rRbm1vX/je+8LqmeIq6piik3Y/zDuZAz0UYWJhQtW8rOnw1jcPDPilwYUChp0un72ai0NHh0sKtT7BHz4fUNc0aDW5Pv1W5/YPf3/i08IQKRYG4PQkLJxt6fjSe38atKXJKD23VeHB7BuSJ0Q5ljAoetp69YVLo6BB0y4cTn/4BQMgdP+zrVKLluO5a+yNTi0GvMDJPPdukrGeFtdsljU9XZT3bUEw9G77iDSrWr8r64R89Tbafq4aD29N7VW4b/scbOW14we5SCdrwEoj0CWZLn0UYWphQt08r+n8+je0jP9HqCwMNBrenZ54Y/VlEjHLaotLbbvePJ2BXrzI7hn9ceistI9JeaybtdUF1B7en6+rcunVgorJuFdIffMKfAQrE8dh7G+n+6VSmXN1AVkYmYbcfcn//ReyUU3PZN65G0zd68Xu/xU+7O9qjhGXIvG9nKswcS9Cs5WRGxRaaxnrycMz7dubRhPlkv6DTmhZGU3kRQhSk9RcFAIyMjOjRowc9evRg6dKlTJkyhY8++ogZM2bg5OTEqVOnCixjZWWl+ltfX1/tO4VCQVZW4T/0FtWhyc7OVvvB39jYWO29nZ0d1tbWahcOStv27dv58ssvOXXqFJUqVVJ9npWVxYABA1i7dm2BZZycnFR/P0kcAFavXs3y5cvVPhtm2ogR5i5FLJEr8Og1Iq/lXpjRMcgpakb2lqTkeQiXUQWLAnd555USHoORvfodpYa2FqRE5IweSIuKJysjU3XHuGq9tsWvtzChp2/xT7s5GNiYkZ2RRXpcEgPdvyXBP/yJ1iPKTsCxa0Rczy1XunnKVXLecmVrUeDu5LxSwmMKjCQxsrUgOaLkzwJIjYonKz2D2HzDUGO9ArHTsimn7vzPDf8bD1TvdQ1y2gILeyvi89xRYWZrSXyE5nrTeWp/us8czPdjVhLs4V/q+S1P6VHxZGdkop+vTdG3tSQ9IqZc8lQe/I9dI7yQumZsV0hdK6bMJIfHYGyvua5lpWcS/zDnrq7Im77YNq1Bgym9ufjBFlUahZ4unTfOxqyKHUdfW62VowSkrmnmefwagXnKlp6ybJnZWao9pNO0ggWJJYhRUZwaV8fMzpIpBz9Rfaajp0vVNvVoNaEnq2pPIDtLO08U7+eLkW4xMUp4hhgBxIfFEO6lfhwLfxBI/T6tnmm9Zen2/9zwy1PP9JT1zNzeirg89cy8hPWsy9T+9Jg5mO/GrCSoiHo2bNlEGnVvydevLSP2BXhIpdfxawQVUYYS85Qhk2esZ49lpWcS7ZfThofc8sWpSQ1avdGbIwu3aFiy/DwoIkamhcQo/93KT6vb8vHU6t6c31/75IV42Km015pJe12Qz/FrhOSNiWFu3UrKExNjDXUrKTwG03znazn1MbcPGesXxp+vrUTP2BADc2OSwmLo8+0s4pTn7xVb18XE1oJJF9erltHR06XD4jE0m9Sbn19575n2tSxkxsSRnZGJnq36w5J1bazIiCz+Qd1mfTri8Mm7BL+3iqSLhY8AsH5jGDZvjiJg0gLSPH1LLd/l6fH5m0G+8zcDW0vSSqn9FlrkPzq3f3l5IS4K5NegQQP2799P8+bNCQkJQU9PT+2hu8+67oyMDC5fvqyaPigyMhJPT0/q1y96DjcdHR1GjhzJr7/+ykcffVTguQKJiYkYGhoW+lwBfX19MjOLHwJz8eJFpkyZwqZNm2jbtq3ad82bN+fPP/+kWrVqha7/aS1YsIA5c+aofXagzpslWjYjMYWExBS1z5JDo3Hs2JiY2zlPd9fR18WuXT1urtxZ5HoiXR/g0LExnj8cUX3m2MmFiKuegPIk5KYvjh0bEZjngbAOHRsTeNStRHnNLy0qZ55Y+1caYGRrQdCxaxqWEM9L4eUqBqeOjYjOU64c2tbj+spdRa4n3O0BTh0bqe7wB3Dq1JgI15LPf5uVnkmkuw8WNZ3UPjev4URiQEQRS5WP1MQUUvPFLS4smjqvNibwzkMAdPV1qdmmPgfX/Fbsurq82Z/us4byw4RVBNzyKassl5vs9AwSbvpg1cmFqMNXVJ9bdXIh6siL/0DXkspITCE+X5lJCo2hYsdGRN3JrWuObevhuqr4ulaxQyPu5qlrFTs2JkxTXVMoVCfWkHtBwKK6A0dGrCI1Wjvn85a6pllaYgpp+WIUHxZN9VcbE5KnbFVtU48Ta4ruH2jie/4OG3t8oPbZwM/eJMI7mAvf/621PzBBToyiColRzTwx0tXXpVqbehx/hhgBPHLzpEIN9eNYhepOxAZq13Esr8LqWWxYNHULqWd/a6hnXd/sT89ZQ/l+wioeFVHPhi1/A5derdgwagVRAS/GjSKF1bOEsBiqvdqI0Dz1rEqbepxcU3Qb/rQU+dpwbVRcjMLyxKhym3qcLoUYdV8xntq9WrJz5EpiH7245Ujaa3XSXheUnphCbL6YJIbFUKVDI8LzlJtKbepxrpi6FXztAVU6NOL6T7l9yCodGxNcyDOnMpJTyUhOxdDShKodG3NudU6sPf48z6Oz6s+oGLx9Ph57z3Pnj6Kn1ilX6Rmk3PHCpH0zEv53QfWxSftmJP57qcjFzPt2xmHlewTPW0Pi6SuFprGeNByb6aMJnLqI1Dsv1nNfipOdnkH8TR+sO7kQme/8LfIlOn8T4mlodW8tMjKSESNGMGnSJFxcXDA3N8fV1ZV169YxaNAgunfvTrt27Rg8eDBr166lbt26BAUFcejQIQYPHkzLli2feJu1a9dm0KBBTJ06lU2bNmFubs6HH36Is7MzgwYNKnbZVatWcerUKdq0acPKlStp2bIl+vr6nD17ltWrV3P16lW1EQyPVatWjRMnTvDKK69gaGiItbX6VeGQkBCGDBnCqFGj6NWrFyEhIQDo6upiZ2fHzJkz+fHHHxk9ejTvv/8+tra2PHjwgJ07d/Ljjz+iq6t5up/CGBoaqp7V8FhJpg4qiuePR6j/9kDifUNI8Amh/tuDyExOw29v7sGuzdfTSQqJ5pbyRybPzUfoum8J9Wb2J/CoG869WuDQoSEnBq1QLXN/02HafPMWUe6+RLh5UXNsV0ycK+D9ywlVGgMrU0ycbVXPBzBX/pCbEhajGlFQfWRH4ryCSImMw7ZlbZqtGIfnD0eI9w5+6n0uL0lJyfgH5A7ZDgwKxcPTG0sLc5wc7csxZ6Xv3uYjNJo9kHifUOJ8Q2j09kAyktPw3Zdbrtqvn0ZSSDQ3VucMt/XYfJSeexfTYGZ/Ao66UalXC5w6NOTo4Nyh3HomhphXd1C9N6tsh3XDKqTGJJIUGAnA3e8O8erGWYRd8iDkwj0qdnGhUo9mHB9e8Hkl2ubMlsN0nzmYiIchhPsG033mENKSU7n213lVmtGfzyAuNIp/1uV0rLtMG0CfOa+x/Z1viAoIx1x5905qYgppSTlTjRmYGGJbLff5KDaV7anYoCpJMQnEBEU+xz18ekGb/qb2N7NJcPch3vU+jmN7YOhsS8gvxwCouvB1DJwq4DX7G9Uypg2rAaBraoR+BUtMG1YjKz2DZOV8wQp9PUzq5Izw0tHXw9DJBtOG1chMTCHlYcjz3cGndHfzEVxmDyTON6euuczOqWs+eeraq+unkRQczbU1OXXt7k9H6fPnYhrN6M+jo25U7tWCih0acmhIbl1r/uFrBPzrTlJQJHpmRlQf1A7HdvU5PmYdAApdHbr88DYVGlfjfxM+z3nWy+OyF5NAVrp2zyspdU2zKz8d4dWZA4l6GEKUbwivzhpEekoat//KLVuDvphOfEg0/67L6R/o6OtiVzunTuka6GHuaI1Dg6qkJaYQ7RdKWmIK4fnm605LSiU5Or7A5y+CSz8docPMgUQqY9RBGaNbeWI05IvpxIVEc0IZI91CYuSojFGU8q7ui5sPM3nvR3SYOZA7By/j3LQmLV7vwt8Lfnr+O/kMTm85TI889azHzCGkJ6filqeejfl8BrGhURxU1rOu0wbQb85r/FJMPRvx8SSaD3qFzVM/IyUxWZUmJS6J9BdsmpOrPx2h/cyBRD8MJco3hPazBpKeksbdPGWo/xfTiA+J5vS6nDZcR18X29rOQE4ZMnO0wb5BFdITU1UjAzq9/xrep9yJD47EwNSI+gPbUaVtfXaNX/f8d/IZuf50hLbKGEX7htB21kAyUtK4lydGfb+YRkJINGeKiJG5MkZpianEKGPU45OJ1B/Yjn1TvyQtMUV193NqXNILNy2ctNeaSXtd0PWfjtBq5kBifEOJ8Q2hlbL9ub8/NyY9v8ypWxfW5tStG1uOMnz3Ylq81R+fY27U6NmCyq82ZPew3D5klY6NUSgURPsEY1XNgVcXjibaJ5i7yh/8U2ISSMn3YPis9EwSw2OI8dHec/zobXtxWvM+Kbe9SLlxD8vX+qDvZE/Mrn8AsH3vDfQcKhDyYc7UTOZ9O+O4Zh5hqzeS4u6BrnKUQXZKKlkJSUDOlEEV3h5PyLy1pAeGqtJkJSWTnZRSSC5eLIGbDlL3m9nEu3sT5+qJ09juGDnbEqw8f6u28HUMnWy4P3uDapm8528GFSwwbViN7PQMkl7AdkeIp6XVFwXMzMxo06YNX375Jd7e3qSnp1O5cmWmTp3KwoULUSgUHDp0iEWLFjFp0iTCw8NxdHSkY8eOODg4aN5AEX7++Wfeeecd+vfvT1paGh07duTQoUMFpt/Jz9ramkuXLrFmzRo++eQT/Pz8sLa2pnHjxnz66adYWhb+ENPPP/+cOXPm8OOPP+Ls7MzDhw/Vvvfw8CA0NJRt27axbds21edVq1bl4cOHVKxYkfPnz/PBBx/Qq1cvUlNTqVq1Kr1790ZHR+ep41DaPL49iK6RAS1WT8TA0pTI696cHrWGjDx3Epg4V1C7GyTS1YuL0zfQ+MMRNJo/gkS/UC5M/4aoPEMSHx24hKG1GQ3nDMHI3orY+wGcHfspSXnu1q7YswVt1k9TvW+/aTYAtz/7kzuf7wVyLhQ0XjgSAyszkh6Fc/frv/DcdLjM4lGWbnt4MWl27h036775AYBBfbqzcvHc8spWmbj77UH0jAxovXoiBpYmRFz35sTotWrlytTZVq1cRbh6ce6tDTT5YARN3h9Ogl8oZ6dvIDJPuarQpAY9/lyket9y+VgAvHed4eJ7OfF8dMSVKx9uoeGsgbT8eDxxPsGcmbqe8CueZb3bz+zfjQfQNzJg2MeTMLY0xf/GAzaNW6V296W1s63alGqvjOuJnqE+EzeqjyA6+tUejn61B4DKLjWZuXOp6rvBS8YDcGXPaXbO+74sd6nURPx1AT1rcyrPGY6BvTVJHv7cHbOKVGWbou9gjWG+B6M1PfGZ6m+zJjWxG9aBlEdhuLWaAYCBo7VaGucZg3CeMYjYC3e4PVT756cGuP1dTl1ru2oihpYmhF/35tjr6nXNrKIt5Klr4a5enJ6xgebzR9Ds/eHE+4Vy6q0NatOAGdla0PHr6RjbW5EWn0T0vUccH7OO4LO3ATB1sqFKrxYADDq+Si1PR4avJORi2U3bVxqkrml2YWNO2erzyUSMLUwJvOHNjrFr1O5Qtaio3j8wd7DmzcO55aH9tP60n9afhxfv8uso7b8w+6TObzyIvpEB/ZQxCrjhza/5YmRZSIym54nRK9P684oyRluVMQq66cOuN7+i2wcj6fT2EKIDwjmyfDu38vxQ8yI4oaxnwz+ehImlKX43HvC9hnr2qrKeTcpXzw5/tYcjynr26rieALy9S72d3jHve67sOV1Wu1MmLinrWa9PJmJkYULQDW92jl2br57ZFihDk/OUobbT+tF2Wj/8Lt7jN2UZMrWzYMCX0zGztyI1Pokwj0fsGr+Oh+duP7+dKyVXlPWshzJGwTe8+UNDjMwcrJmYJ0atp/Wj9bR++F+8x05ljJqN6w7A6D/U5zU/NHcTt/ecLctdKnXSXmsm7XVBbt/nlJsuKydiaGFCyA1v9o9ZS3qemJjnq1vBbl4cnrWBdvNG0G7ucGL9Qjk8cwOhN3L7kIYWJrT/4DXMHG1IjU3kwaErXPh0N1kZ2n3DiCYJh88QZmVBhRlj0LWzJs3Lj8DpS8gIynkepK6dDXpOuTf6WY7si0JfD4els3BYOkv1eey+44Qu/BwAq9ED0DEwoOLX6g/zjtywnchvtz+HvSpb4X9dQM/ajKrK87dEj0fcznP+ZlDI+VuLE7nPSTFvUhN75fnblVYzn2vexRMqZgp08eQU2c/yVCDx0tnlNKa8s6D1ht7S/geHlbddLks1J3rJXTPIKO8saL1hKRIjTbx0jco7C1rtpr6UIU2ss59+hODL4sX+6eH5iFZIlDRxzNbqe7W0grRGmqWWxpOj/+OkNdLMOluhOdFLro+pdk3bpI1Cos3LOwtar2PI7vLOwgsn5VLpT3tYUkZtR5bbtsuK9D6FEEIIIYQQQgghhBBCaC950HCp0p65ZYQQQgghhBBCCCGEEEIIUabkooAQQgghhBBCCCGEEEII8ZKQ6YOEEEIIIYQQQgghhBBCaC950HCpkpECQgghhBBCCCGEEEIIIcRLQkYKCCGEEEIIIYQQQgghhNBeMlKgVMlIASGEEEIIIYQQQgghhBDiJSEXBYQQQgghhBBCCCGEEEKIl4RcFBBCCCGEEEIIIYQQQgihtbKzM8vt9TS+++47qlevjpGRES1atODs2bPFpt+xYwdNmjTBxMQEJycn3njjDSIjI59q2yUhFwWEEEIIIYQQQgghhBBCiFKwa9cu3n33XRYtWsT169fp0KEDffr0wd/fv9D0586dY/z48UyePJk7d+6we/durl69ypQpU8osj3JRQAghhBBCCCGEEEIIIYT2ysoqv9cT+uKLL5g8eTJTpkyhfv36fPXVV1SuXJnvv/++0PSXLl2iWrVqvP3221SvXp1XX32VadOm4erq+qxRK5JcFBBCCCGEEEIIIYQQQgghCpGamkpcXJzaKzU1tdC0aWlpuLm50bNnT7XPe/bsyYULFwpdpn379gQEBHDo0CGys7MJDQ1lz5499OvXr9T35TG5KCCEEEIIIYQQQgghhBBCe2Vnldtr9erVWFpaqr1Wr15daDYjIiLIzMzEwcFB7XMHBwdCQkIKXaZ9+/bs2LGDkSNHYmBggKOjI1ZWVnzzzTelHsbH9MpszUK8pHa5LC3vLGi9kTdXlHcWtN63TSaXdxa0nqOhc3lnQetVTcsu7yxotRqZ0g3SpEFaWnlnQevdMjQo7yxoPfssqWuaPPmgdCEKuqObUt5Z0Ho904zLOwtaz1+/vHOg/f5JtC3vLGi9K4YJ5Z0FrdexvDMgnsiCBQuYM2eO2meGhobFLqNQKNTeZ2dnF/jssbt37/L222+zdOlSevXqRXBwMO+//z7Tp0/np59+erbMF0F66EIIIYQQQgghhBBCCCFEIQwNDTVeBHjM1tYWXV3dAqMCwsLCCoweeGz16tW88sorvP/++wC4uLhgampKhw4d+OSTT3Bycnq2HSiETB8khBBCCCGEEEIIIYQQQnu9IA8aNjAwoEWLFhw/flzt8+PHj9O+fftCl0lKSkJHR/1nel1dXSBnhEFZkIsCQgghhBBCCCGEEEIIIUQpmDNnDps3b2bLli3cu3eP9957D39/f6ZPnw7kTEc0fvx4VfoBAwawd+9evv/+e3x8fDh//jxvv/02rVu3pmLFimWSR5k+SAghhBBCCCGEEEIIIYT2yn5xnsI0cuRIIiMjWbFiBcHBwTRq1IhDhw5RtWpVAIKDg/H391elnzhxIvHx8WzYsIG5c+diZWVF165dWbt2bZnlUS4KCCGEEEIIIYQQQgghhBClZMaMGcyYMaPQ77Zu3Vrgs9mzZzN79uwyzlUumT5ICCGEEEIIIYQQQgghhHhJyEgBIYQQQgghhBBCCCGEENrrCR/4K4onIwWEEEIIIYQQQgghhBBCiJeEjBQQQgghhBBCCCGEEEIIob1eoAcNvwhkpIAQQgghhBBCCCGEEEII8ZKQkQJCCCGEEEIIIYQQQgghtJc8U6BUyUWBUjBx4kS2bdsGgJ6eHpUrV2bo0KEsX74cU1PTp1rnsmXL2L9/Pzdu3CjFnJavWhO6U3dGP4ztrYj1DOT60l+JuHy/yPR27erRdNlYLOs4kxwag8d3B/H+5YRamkr9WtFo/gjMqtqT4BfGrTV/EHjYNXcdbetR961+2LhUx9jRmnNvfEHgETe1deiZGOKyaBTOvVtiYG1GUkA4npuPFthWeXKZO5RaY7pgYGlK5HVvrizcSqxnYLHLVO7biibzh2Ne1Z54vzDc1+zm0ZHc2Ni3qUuDGf2waVwdE0drTk36koB8sQGwqFWR5otHYd+2HgodBTH3Azk7/RuSAiNLfT+fJ9cbt/j5tz3c9XhAeGQU61cvoVvH9uWdredq8pwJDBrTHwtLc+5cv8dni9bj6/mwRMt2H9iFj79fyukj5/hw8hK1dU6ZO1EtbWRYFP2bDSvFnD8/r7w7lCavd8HQ0pTg694cX7KVSK+i616F2s68OncYjo2qY1nZjhPLf8Vty1G1NG1mDKBO71ZUqOlEekoaQW5enF6ziyif4LLenWI1nDuUmmO7om9pStT1B7gt2EqchnZGUxsMJWv7NW3byM6SJktfx6FjI/TNjIj3Dubu+gME/HNFlca6cTVcFo3CpmkNsjOzCDh0lRsfbScjKbUUolNyrd8bSsMxOWUm9Lo3pxdvJUpDHGv2aUWbecOxrGpPrF8Ylz7djU+e9lqhq0PrOUOpO7g9JvZWJIbG4LH7DFe//guys9HR06XN+8Op1rUpFlXsSItP5tHZ21xcs4vE0Jgy3uMn4zyxJ1VmDsTA3orE+wF4LdlK7GWPItNbtatPreUTMK1bibTQaPw2HCDol+Oq7xV6ulR9ezBOIzth4GhDkncQ3h/vIOqke+462tanysyBmLtUx9DRhpsTPyXi8NUy3c9n1ea9oTR6vQtGlqaEXPfm5BLN5ahWn1a0nTccyyr2xPqHcXHdbryP5pYjfVMj2s0bTs1eLTGxtSDs9kPOLNtO6E2fQtfXdfUkGo/pyunlv3Ljp6OFpilPrd4bSoM8de3M4q1Ea4hRjT6taJ2nrl3+dDe+R9TbLFNHa9otGEWVLi7oGhkQ6xPCyfd/JPzWQwCMbS1ot3AUlTs2xsDChODL9zm7ZBuxD0PLaldLTXm0T9quLGIy/sKXWFS2K7DczW3HObN4m+q9da2KtF84ioptcvrYUZ6BHHnrGxKCtLuPPfjd1+g8ugemlqZ43/Di1yWbCfR6VGT6TqO688rQTlSqWwWAh7d82PPpDnzcH6jSGJkaMXTuaFr0bIOFrQV+d3zZsXwLvje9y3x/SkPjfOdqV5/gXO1xH+rGmt0E5DtXq5/nXO10Iedqbb98k5ojO6p9FuH2gKMDlpXavpWVV98dSlPlcS7oujfHlmwlopg+tm1tZzoo+9hWle343/JfuZqvj91sbDeaj+2GZaWc+hfhFcC59fvwOXWzTPeltLR9byiNlTEJVh77I0tw7G+f59h/vpBjf/t5w6mV59h/Kt+x/z3/7YWu+8zK33Hb9E/p7FwZGfbuKLq93hNTS1MeXPfi5yWbCCimPWrVuy2DZw7HoaoTuvq6hPgG88+Pf3Fu3ylVmkEzhtGqd1sq1qxEWkoqnm73+X3NNoJ9gp7DHgnxfMn0QaWkd+/eBAcH4+PjwyeffMJ3333HvHnznng92dnZZGRklEEOi5aWllbm26g8sC1NV4zj3vq/ONpzERGXPei4Yz4mzhUKTW9a2Y6O298n4rIHR3su4t7Xf9Hs4/FU6tdKlaZCi1q02zgbvz3nONp9AX57ztF+02xsmtVUpdE1MSTmrj9ui7YWmbemK8bh2MWFS7O+43DH97n/w2Gar5xAxV4tSm3/n0WDmf2p92Yfri7axuG+S0kOj6Hbzg/RMzUqchnbFrXosHEWvnvO8U+PhfjuOUeHTbOokCc2eiaGRN/x5+qibUWux6yqPb32LyH2QRDHh6/kn+4Luf3VfjJT0kt1H8tDcnIKdWvVYOGcGeWdlXIxdsYoRr85gs8Xf82kftOJDI9i/e+fYmJqrHFZR2cHZi99i+uX3Av93tvDl35Nh6peY7tNKu3sPxetp/en5ZQ+HF+6jV8HLCUxPIaROz7EoJi6p29sSKx/OKfX7iIhLKbQNJXb1Of6L8f5dfAy/hi7Fh09XUb8+gH6xoZltCea1ZvZn7rT+uK2aCv/67OElLBYOu9aUGw7U5I2uCRtf0m23eabtzCv6cS5CZ9zpMuHBBxypd2m2Vg1qgqAkYMVnXYtIOFhKP/r9xFnXl+HZZ1KtF4/vQyiVbTmb/Wn6dQ+nF68jT/655SZQb99iH4xcXRsXote383i/t5z/N5rIff3nqPXd7NwaJobx+Yz+tNobDdOL/mFHV3mc2HV7zSb3g+XN3oCoGdsgF2jalxdv59dfZZwaOpXWNVwot+WOWW+z0/CflA7an88kYdf7eVq9w+IvXyPJr8vxLCIvoBRFTua/LaA2Mv3uNr9Ax6u30edlW9g16+NKk2ND0fhPL4Hngt/5nLHOQRtO07jn9/HrFE1VRodE0MS7jzEc8GWst7FUtHirf40m9KHU0u2sVNZjobs0FyO+nw7C4+95/it90I89p6jT75y1H3dFKp0aMTRd79ne48F+J+9zZDfPsTUwbrA+mr0bIFj05okhESVyT4+q2Zv9afJ1D6cXbyNPf2XkhQew0ANdc2heS16fjcLz73n2NVrIZ57z9Hzu1nY54mRoaUJQ/YuJTMjk4PjP2Vn1w84//FvpMYlqdL02fweFlXsOTz5S3b3Xkx8QAQDf1+AXjm24SVRXu2TNiurmPzRfylbms9UvfaPXg2A98HcC9kWVe0ZtncJ0Q+C2PfaSnb2WsjV9fvJTNXuPnbf6YPpPXkAvy7dzLKBHxAbHsP725diVEzM6rVtyKUD51gz+iM+HrqQyKBw5v26FGsHG1WaSWtn0OjVJvww52sW9ZrD7bPuzN/+kVoabdVgZn/qv9kH10XbOKI8V+tagnO1V5XnaoeKOVeLueOPazHnagBB/7rzZ5OZqtfJcZ+W2r6VlbbT+9N6Sh+OLd3GVmUfe1QJ+tgx/uGcKqaPHR8cxam1u9g6YAlbByzh4YW7DP9xDra1nctoT0pPy7f603xKH04u2cZvyuPaUA3Hfqfmtej37Szu7T3H9t4Lubf3HP2+m4Vjnvaox7opVO3QiCPvfs8vPRbgd/Y2w/Id+ze1mKn2Ojr3B7Kzsnhw+Ephm9UaA6YPoe+Ugfy89AcWDXifmPBoFu5YXmx7lBCTwL4Nu1k69AM+6PUup3efYPpns3Hp2FSVpn6bhhz75TBLB89n1dhl6OrpsODXZRhq+XFeiKchFwVKiaGhIY6OjlSuXJnXX3+dMWPGsH//frZv307Lli0xNzfH0dGR119/nbCwMNVyp06dQqFQcPToUVq2bImhoSG//vory5cvx93dHYVCgUKhYOvWrQAoFAo2b97MkCFDMDExoXbt2hw4cEAtL3fv3qVv376YmZnh4ODAuHHjiIiIUH3fuXNnZs2axZw5c7C1taVHjx5lHp+60/rg+/spfH47RbxXENeXbic5KJKaE7oXmr7m+G4kBUZyfel24r2C8PntFL47T1N3ej9VmjpT+xB65jb3vjlA/INg7n1zgNBzd6gztbcqTci/7txeu5vAQ66FbQbI6ZQ93H2W8Iv3SAqIwGf7SWLu+mPTpHrpBeAZ1J/Sm9tf/8Wjw67E3g/gwjub0DM2oPqQou9qrze1N8FnbnNnw9/EPQjmzoa/CTl3l/p5YhN08ibu6/bw6HDRsWn64QgC/3Xn+ic7ib7tR4J/OIEnbpAaGVeq+1geOrRrxdtvTqBH51fKOyvlYuSU4Wz9ejunD5/F5/5DPn53DUbGRvQcUnidfExHR4dlGxax+bOtBPkXfmd7ZmYmUeHRqldMVGxZ7EKZazm5Nxc3/IXXEVciPAM4NHcTekYG1B9UdN0LuenDqVW/4/H3pSJP7PdMWMftPWeJ9Aok/J4/h+b9gGUlWxwaVyujPdGsztTe3F2/n8BDOe3M5Xc2omtsQNWhRe9rSdrgkrT9Jdl2hZa18dpyjKgbPiT6h3P3q/2kxyZirYxZxR7NyM7IxG3BVuK9g4ly98Ft4VYq92+NWTWH0g9YEZpM7o3rN3/hc8SVqPsB/O+9nDJTZ3DRcWwypTePzt7G7du/ifEOxu3bvwk4f5cmU3Lj6NS8Nr7H3PD79wbxARF4H7rKozO3sHfJOU6lxSdzYMxaHhy8TIxPcM5d00t/wd6lBmYVC//BvTxUnt6foN/+JXjHvyR5BeK1ZBupgRE4Tyz8x0Pn8T1JCYjAa8k2krwCCd7xL8G/n6TKjAGqNI4jOvBw/T4iT1wnxS+MwG3HiTrlTpW3ctNE/XsDnzW7CD+k3Se3jzWb3JurG/7C+4grkZ4BHJ+zCX0jA+oWU46aTe6N/9nbuH77N9Hewbh++zePzt+l2eSccqRrqE+tPq04t2onQVfuE+sXyuUv9xL3KByXcd3U1mXqYE3njydw5J3vyErPLNN9fVouk3vjlqeunVDWtdolqGvXlHXt2rd/E5ivrjV7awAJwVGcnPsDYTd8iA+IIPD8HeL8cvrultUdcWxRm9MLfybM3YcYn2DOLPoZfVNDag9qV+b7/SzKq33SZmUVk5SoeJLCY1Wvat2aEfMwlMBL91Rp2s4fwcN/3bmwaicRd/yI8w/H798bJGt5H7vXpP4c+PZP3I5eJtDzET/O/QYDY0PaDupQ5DKb3l3Pv9uP4n/3IcHegWz5cCM6CgUNXmkMgL6hAS17t2XX6l+4f+UuYX4h7P/qD8IDwug6ttfz2rWnVi/fudpF5blaNQ3naiGFnKvVe8JzNYDMtHRSwmNVr7SYxFLbt7LSanJvLmz4C09lH/vg3JzjXINi+tjBN304uep37v19iYwi+tgPTlzH+6Q7Ub4hRPmGcObT3aQlpVCxea2y2pVS03xyb65s+IsHymP/0Tk57VE9Dcd+v7O3uao89l8t5Nhfu08rzq7aSaDy2H/py73EPgqnSZ5jf972Kik8lpo9m/Po4j1i/cPLfL+fRZ/JA9i/YTdXj1wiwNOf7+eux8DIkFcGdSxymXuXbuN69DJBDwII8w/hyM8H8fd4SN1WDVRp1kxYwZk9/xLg9Qj/ew/ZOO8b7CrZU71xzSLXK56jrKzye/0HyUWBMmJsbEx6ejppaWl8/PHHuLu7s3//fnx9fZk4cWKB9PPnz2f16tXcu3ePnj17MnfuXBo2bEhwcDDBwcGMHDlSlXb58uW89tpr3Lx5k759+zJmzBiionLu5AoODqZTp040bdoUV1dXjhw5QmhoKK+99pra9rZt24aenh7nz59n06ZNZRoLHX1drF2qE3L6ltrnIadvYduydqHLVGhZu2D6UzexaVIdhZ6uMk0tQk7fLJDGtlWdJ8pf+BVPnHs2x9gx52q5ffsGmNdwJEQLhhmaVbHD2MGK4DyxyErLIPSSR5GxA7BrUUttGYCgUzeLXaYAhQLnbk2J9wmh62/zGX7zW3ofXEal3toxgkI8vYpVnLB1qMCV07knGelp6Vy/5E7jlg2LXXbSe+OJiYzh752HikxTubozB9x28+fF31jx3RIqVnEqtbw/L5aV7TCzt+Lh2dx6lJmWwaPLHji3eIJ6VAKG5iYApJTTSZxpFTuMHazV2tystAzCL3pQoZg2Q1MbXJK2v6TbjrhynyoD22JgZQoKBZUHtUXHUJ+wCzk/sOga6JOVlqE2VUVmSs4oONvWdZ8oHk/Looodpg5W+J9R35fAyx44FVNmHJvXUlsGwP/0TRzzLBN01ZNKrzTEqrojABXqV8GpVV38ThY+WgfAwNyY7KwstTucy5NCXxdzlxpEnVLPc9Tpm1i2LPz/yLJlbaLylbHIkzcwb1JD1RfQMdAnK1V9xGNWShqWz+n/vbRZVLHD1F69HGWmZRCgoRw5FVGOHi+jo6eLjp5ugYuVGSlpVGyVJ1YKBb2+ms61Tf9onEKlvDyua4/y1bWgyx5q9SY/h+a11JaBgnWtWo/mhN/0oef3s5l4/VtGHP6E+qM7q77XNcyZeTVvHLOzsslMy8Sp9ZP1P58nbWuftEFZxiQvHX1d6g59hXu7Tud+qFBQrWtTYnxDGLh9PpOuf8vwA8uoriWjlItiV9kBK3trbp/N/b/NSMvg/uU71G5R8jbX0NgAXX1dEmISANDV00FXT5f0fO1TekoatVvVK53Ml5HiztXsiulD2RZyrhZ86maxyxTFoV19ht38lgFnP6XNp5MxrGDxxOt4nqyUfWzffH1s/8seVCrFPrZCR0H9AW3RNzYk8JpXqa23LFgqj/1++Y79gZc9qKjh2O+Xrz16ePqmapnHx/78F1EKHPvzMLG1oHrXptzeeeop9+b5sK/sgLW9DbfO3lB9lpGWwb3Lt6nTouTtRsNXXHCq4YzH5TtFpjFRnqs9brOE+C+RZwqUgStXrvDbb7/RrVs3Jk3KnTajRo0afP3117Ru3ZqEhATMzMxU361YsULtjn0zMzP09PRwdHQssP6JEycyevRoAFatWsU333zDlStX6N27N99//z3Nmzdn1apVqvRbtmyhcuXKeHp6UqdOzglLrVq1WLduXanve2EMbMzR0dMlJVz9buGU8FiM7CwLXcbYzpKQQtLr6OthaGNOSlgMRnZWpITH5UsTV+Q6i3J98TZafjaFgdc3kJWeQXZWNlfnbSbiiucTracsGNlbARQaO9NKtkUvZ2dFSkS+ZSJiMX6C2BjZWqBvZkzDWf25sXYP11fupGKXJnTa/A7Hh68i7FLRc0AL7VbBPmcodlREtNrnUeHROFYq+q5ql5aNGDC6L+N7TCkyzZ3r91jxzhoe+TzCxs6aiW+P44e/NvB61zeIi9buu9/yMlXWvaR8dS8pIhYL56Lr3tPoumQMj67cJ8IzoFTXW1JFtjMRsZhoameKaYNL0vaXdNsXp31Du02zGXLvB7LSM8hITuP8pC9JVN69G3ruDk2XjaHuW/3w2nwEXRNDGi/IuRhu7GBVgig8OxO7nO0k52t7k8NjMS8mjiZ2VgWXiYjFNE97fe27vzE0N2bMqXVkZWaho6vDpXW78frrYqHr1DXUp/2CkXjuv0h6QvJT7lHp0rexQEdPl7R8/9dp4bEYKMtBfgb2VoWm19HXQ9/GnLSwGCJPuVN5Wn9iLt4j+WEo1h0aYdurJQrdF/O+F1NlOUqKeLK2x8TOqtBlTJTlKD0xhSBXT1q/PZioB4EkhcdSZ1B7HJvVJMY3dy78ljP6k5WZxY0t2vcMgcdMiorRU9Y1kzx1zaKKHQ3HdsN98xGubTiAfdOadFgxnqy0DO7/eY6YB8HEPQqn7QcjOb3gJ9KTUmkytS+mDlaYFFGOtYE2tU/aoixjkleNXi0xtDDBY/eZ3HXYWmBgZkyLGf259OkeLqzaSZXOTej7wzvsG7mKIC3tY1sqYxYXHqP2eVx4LBUqFXyGQlFGfDCW6JAo7p7PueibkpiCl5sHA98eTtCDAGIjYmk38FVqNK1NqG/5Pm9Jk9I+V3vS89jgk+74H7xCYkAEZlXscJk/nO67F3C495KcmyW00OM+dmK+mCVGxGJZCn1su7qVGL9vGXqG+qQlprB32ldEemn3XPBFHtciYjEvJiamJTz2t8lz7K87qD1OzWoS7Vv4c3AaDO9AemIKD44UP0KlvFkqy1FsvvYoNiIWW+fi2yNjcxO+u/wTegb6ZGVm8fOSTdw6V/SF7HFLJuFx5S4Bnv7Pmm1RGrL/m3fslxe5KFBKDh48iJmZGRkZGaSnpzNo0CC++eYbrl+/zrJly7hx4wZRUVFkKYec+Pv706BB7hClli1blnhbLi4uqr9NTU0xNzdXTUnk5ubGyZMn1S44PObt7a26KFCS7aWmppKaqv6AxvTsTPQVuiXOq5r8DxtTKDSkz59cUXA9+dapUBSyHQ1qT+5Fhea1ODv+MxIDIrBrW48WqyeSEhpN6NmirxiXhWpD2tNmXe6FpJPjPsv5I/8uKRQFP8uvwPeKJ4qNQicn3o+OXsPjxyMARN/xx65lbeqM7yYXBV4gPYd054O1uXOLzxu/AMh5hklexdUfE1NjPvpmIavf/4zYYn7cv3Qyd3oObw9fbrneZc+FHfQd0YudP+x+hr0oWw0Gt6fnqty69+cbOXWvYDVSlOpzE7t/PAG7epXZMfzj0lupBlWHtqfFusmq92cfzz1baDujYWdLUoZK0vZr2HbjD0ZgYGnKyRGrSIuKx7l3S9r/8Db/Dv6YWI9HxHkGcvmdTTRdNgaXhSPJzszC66ejJIfFkJ1ZNh3HOoPb03lNbpk5OFFZZgrZlycMI6BQq5+1B7alztBXODb7O6I8A7BtUJUOy8bmPNBzz1m1JXX0dOn17UxQ6HCqmOfplJ/85YHiy1mBMqbeF/Ba/DP1Pp9O2/NfkZ2dTfLDUIJ3nsJpVOfSy3IZqju4PV1X55ajA0WUI8XTlKN89ejYexvp/ulUplzdQFZGJmG3H3J//0XslNNw2TeuRtM3evF7v8VPuztlona+uvbPxML7RooS9I001TWFjg7hN324vPYPACLu+GFTx5mG47px/89zZGVkcnTaerp8OpXJt38gKyOTgHN38Pv3xtPtXBnR1vapPD3PmOTVYFQn/E66qz30/XEf2/fYNdw35/SxI+7649SyNo3GdtOaiwLtBnVg4qppqvdfTMq56azAvioK+awIfacNou3AV1kz6iO1kQE/vPc1kz+dyform8nMyMTvtg+X/jpL1UY1nn1HSlG1Ie1pnedc7dS4otvsp2mPnrST6Xfgsurv2PsBRLr7MvjKVzh3a6px2qHnpeHg9vTO08f+o4g+dkliVhKRPsFs6bMIQwsT6vZpRf/Pp7F95CdadWGg3uD2dMtz7N9fxHHtac75FfmO/Ufe20jPT6fyZp5jv8f+i9gXMW1pw9c6cW/fBa17vskrgzsyZdVbqvfr3vgEKDxkmupRSkIyH/Z5DyNTYxq94sLYxZMI9Q/l3qXbBdK+8fGbVKlXjWXDFzzrLgihleSiQCnp0qUL33//Pfr6+lSsWBF9fX0SExPp2bMnPXv2ZPv27djZ2eHv70+vXr0KPNzX1NS0xNvS19dXe69QKFQXG7KyshgwYABr164tsJyTU+4UHiXZ3urVq1m+fLnaZ8NMGzHC3KWIJQqXFhVPVkam6k6Kx4xsLQrcVfFYcngsRvbqd0oY2lqQlZ5BanTOsK2U8JhC06RElPxuZF0jfRovGMn5SV8SfOIGALH3HmHdsCp13+r33C8KBBy7RsR179z8GeRUUSN7S5LzPFDJyNaC5CJiB8rY5LvTxMjWguQniE1qVDxZ6RnE5ps+INYrELsXdFqGl9W5Y+e5e/2u6r2+gQEAFexsiAzLfYikta11gdEDjzlXq0jFKk58ujV3FJKO8qT2rN//GNVxPIF+BTvbKckpeHv4ULm6dj/g68HxawQVUvdM7SxJzFP3TCpYFLgj52l1Wz6eWt2b8/trnzzXh3kGHr1G5LXcfdXJ086k5G1nKhTdRoPmNrgkbf/j7RW3bdOq9tSe3IvDneYTp2yPYu76Y9umLrXe6IHbBzkPj/XfdwH/fRcwtLUgMymV7GyoM60vCf65z/EpTb7HrxF6o2CZMbGzJCnPvhhraK+TwmPU7lR+vExSnva6/aLRXPvub7wOXAIg0iMA80q2tJg5QO1HNx09XXp9PxuLynbsH7laa0YJAKRHxZGVkYmB8m64xwxsLQuMBngsLSymwCgCfWVfIF3ZF0iPjOfWxE/RMdRHz9qMtJBoai4eQ3IZ/b+XNp/j1wjJ2/YY5rY9auVIQ9uTFB5T4E7lnPYqtxzF+oXx52sr0TM2xMDcmKSwGPp8O4s45ZzBFVvXxcTWgkkX16uW0dHTpcPiMTSb1JufX3nvmfb1aT08fo1dJaxr+Ud35VVUXcvbN0oKiyEq3w9H0Q+CqNG3lep9+K2H/NF7EQbmxujo65ESFc+wA8sIu+n7NLtXJrSxfSpvzzMmj5k7V6DSq404/OZXap8nR8WTmZ5BlJd6HzvKK7DIKT3Kw/X/XcX7Ru60K/oGOeeglvbWanfnWthaEhcRgyZ9pg6k/8xhrBuznEcefmrfhfmHsnrkUgyMDTE2MyY2PIYZG+YQ/ki72vKiztWM8/VjDIs5z4WcPlT+EdxGT3geW+h6w2JIDIjAvEbB2QbKi1cRfWyzQvrYiaXQx85KzyTaL+cu+JBbvjg1qUGrN3pzZOGWZ153afE+fo3gPDHRM8xtj57kvCOxsPaokGP/buWx39DcmMSwGPp+O6vQ5wU4t66LTa2K/DNzw9PuWplxO36FB9dzZ3J43B5Z2VkRE5Z7/mpRwZJYDe1RdnY2oX4hAPjd9aVirUoMmjGswEWBicun0qJ7a5a/tpCokMhS2hMhtMuLObZaC5mamlKrVi2qVq2q+tHew8ODiIgI1qxZQ4cOHahXr57aQ4aLY2BgQGbmkz/crXnz5ty5c4dq1apRq1YttdeTXHgAWLBgAbGxsWqvwWbFzzdemKz0TKJv+uLYsZHa5w4dGxPhWvj8fpGuXjh0bKz2mWOnxkS5+5KdkalM86CQNC5EXC35tD8KPT10DfQK3N2SnZWFQuf5V4+MxBQSHoaqXrGegSSHxuCUJ3Y6+ro4tK1XZOwAwt0eqC0D4NSp6HgXJis9k0h3Hyxqqs8Hb17DicSAiCKWEtooKTGZgIdBqpev50MiQiNp1TF3xJCevh7N2jbhlmvhF8L8HvgzpusbTOg5RfU6e+wC1y7cYELPKYQGFd626RvoU612VSJDn9+P3k8jLTGFGL9Q1SvSK5CEsBiqvape9yq3qUeg27PPS9p9xXjq9G7JrtGriH30fB/ilb+difMMJDk0Gsc87amOvi527eoRWUyboakNLknbn+gfrnHbesaGQMG7EHPa6YKjDlIj4shISqXKoLZkpaYReqbgXT+lIT0xhdiHoapXlGcgiaExVO6gXmac29QjuJgyE3LtgdoyAFU6NiYkzzL6xgZkZ+Xb/0z1/X98QcCqugP7R68hRcvmPc1OzyT+pg82ndRvLLDp6EKs6/1Cl4l19cKmY770nZsQ7+6j6gs8lpWaTlpINAo9Xez6tyHiqHbcIalJemIKsX6hqleUZyCJYTFUyVeOKmkoR8HXHqgtAznlqLBlMpJTSQqLwdDShKodG+Nz3A0Ajz/Ps6PnQn7rvUj1SgiJ4tqmf9g37vlMOVmY9MQU4h6Gql7RyrpWKV+MKrapp1Zv8gstpK5VzlfXgl09scrX77Gq4UhCIf2etPhkUqLisazmgJ1LDR4ec3vaXSx12tY+aYPnGZPH6r/WieSIOB4qbzx6LCs9kzB3H6xq5C9rTsQHak8fOyUxhTC/ENUr0OsRMWHRNHo1t13W1dejbpuGeLkV3o4/1ufNQQycPZzPJ3zMw1veRaZLS04lNjwGEwtTGnVsyvXjV0ttf0rDk5yrhRfTh4pwe1Cgf+TUqXGxy5SEgbUZphVtSM4zMqW8pSWmEO0XqnpFFNHHrtKmHgGl0MfOT6FQqC5EaIv8x/5I5bG/aiHtUZCGY3/VfO1R1Y6NC10mIzmVxEKO/Xk1HNmJ0Js+RNzTvmlyUhJTCPULUb0CvB4RHRZF41ebqtLo6utRv00jPN2ebLSVQqFQXWR4bOKKqbTq3ZZPRi/RuouTLz150HCp0q7W8T+mSpUqGBgY8M033zB9+nRu377Nxx+XbIqIatWq4evry40bN6hUqRLm5uYYGhpqXG7mzJn8+OOPjB49mvfffx9bW1sePHjAzp07+fHHH9HVLfnUP4aGhgW2+bRTB93fdJg237xFlLsvEW5e1BzbFRPnCnj/cgKAxgtHYuJozeW3NwLg/csJak/qQdNlY/DecRLbFrWpProzl2bkXrX23HyErvuWUG9mfwKPuuHcqwUOHRpyYtAKVRo9E0PMqufeKWFaxQ6rhlVJi0kgKTCSjIRkwi7cpemS0bglp5EUEIFdu/pUHd6BG8u2P9W+lrZ7m4/QaPZA4n1CifMNodHbA8lITsN33wVVmvbrp5EUEs2N1TnD3T02H6Xn3sU0mNmfgKNuVOrVAqcODTk6OLf86ZkYYl49d/54s8p2WDesQmpMIkmBOVfC7353iFc3ziLskgchF+5RsYsLlXo04/jwlc9p78tOUlIy/gG5dwMGBoXi4emNpYU5To725Ziz52PX5j1MmD2GAN8AHvkGMGH2WFKSUzi273+qNEvXLyA8OJzv12wmLTUdn/sP1daREJfzo2Pez2cvmc654xcJCQzF2taaN94Zi6mZCYd2a+/81EVx/ekIbWcOJPphKNG+IbSdNZCMlDTu/ZVb9/p+MY2EkGjOrMupezr6utjWzhkVoWugh7mjDfYNqpCWmEqM8q6lHp9MpP7Aduyb+iVpiSmqu3tT45IKPAjsefH88Qj13x5IvG8ICT4h1H97EJnJafjtzd3XNl9PJykkmlurduUsU4I2WFPbX5Jtxz0IIt4nhJbrJuO+fAep0QlU6t0Sx46NOPt4ijWg1hs9iHT1Ij0xBceOjWmydDQ3V+4i/Tk+aNf9pyO0nDWQ2IehxPiG0FJZZjz358ax+5fTSAyJ5qJyehL3n44ydM9imr/VH59jbtTo2YJKrzZk79Dc9tr3f9dpOXsQ8YGRRHkGYNeoGk2n9uGu8sGVCl0dem96G7tG1Tg48XN0dHVUd46lxCSQlf7kNxmUhUcbD9Jgw2zi3X2IdfWk4rjuGFayJWjbcQBqLBqNoaMN92Z/C0DgL8eoNLkXtZaPJ2j7CSxb1qHi6125Mz33TnaL5rUwdLQh/s5DDB1tqP7+CBQ6Cvw3/KVKo2tiiHGevoBxFXvMGlYlPSaB1EDtu/Pr+k9HaDVzIDG+OeWo1ayBpKekcT9POer5ZU7bc0FZjm5sOcrw3YtpkaccVX61IbuH5ZajKh0bo1AoiPYJxqqaA68uHE20TzB3/8iZ6zwlJqHAxaSs9EwSw2OI8dGueb1v/nSEFsq6FusbQnNlXfPKE6Nuyrp2SRmjmz8dZfCexTR7qz++x9yorqxr+/LUtZubjzBk31KazxrIg4OXcWhagwavd+HUB7l3mNbs15rkyHgSgiKwqVeZV5eNw/eoK4/K6AJkaSmv9kmblVVMAFAoqPdaRzz2nC10Grvrmw7R69tZBF32IPDiPap0cqF692bse027+9hHtxyk/8xhhD4MJsQ3mAEzh5GWnMqlv3JHhbz5+WyiQ6PYvW4HkDNl0NA5o9n4zldEBISrnk2QkphCalIKAI06NkWhgGDvIByqOTJy4XhCfAI5u/vf576PT8pj8xEazh5InE8o8XnO1R7mOVdrt34ayfnO1XrkO1dz7NCQY09wrqZnYkjjeUN59M9VkkNjMK1sR9MFI0iNStCaqYOKcvWnI7RX9rGjfENorzzO3c3Tx+7/xTTiQ6I5XUQf20zZx05PTFWNDOj0/mt4n3InPjgSA1Mj6g9sR5W29dk1vvwubJfUNeWxP1p57G+tbI888rRHvZTH/vPK9uj6lqO8tnsxLd/qj/cxN2r2bEGVVxvyR55jf9WOjSHPsb+D8th/548zats3MDOmTr/WnPnkt+ezw6Xg8E9/M2jmcIIfBhHiG8zgWcNJS0nl/F+5+/bWF+8QHRLJznU5v+0MmjEMn5sPCPULQc9Aj6ZdWtBhaGe2LN6oWmbSJ9NoP7Ajn09dRXJisqrNSopLIj1VfcYPIV50clGgDNnZ2bF161YWLlzI119/TfPmzfnss88YOHCgxmWHDRvG3r176dKlCzExMfz8889MnDhR43IVK1bk/PnzfPDBB/Tq1YvU1FSqVq1K79690SmHO98fe3TgEobWZjScMwQjeyti7wdwduynJCnvvDK2t8LEuYIqfeKjcM6M/ZRmy8dSa2IPkkOjub7kFwL+yb1bJNLVi4vTN9D4wxE0mj+CRL9QLkz/hqg8Q/Gsm9Sg697cuXGbLR8HgO+uM1x5dxMAF6dvwGXhSNp+OwMDKzOSAiO4tfYPtR+tytPdbw+iZ2RA69UTMbA0IeK6NydGryUjMUWVxtTZVu0urQhXL869tYEmH4ygyfvDSfAL5ez0DUTmiU2FJjXo8eci1fuWy8cC4L3rDBff+wGAR0dcufLhFhrOGkjLj8cT5xPMmanrCdeChzA/q9seXkya/YHq/bpvcvZ5UJ/urFw8t7yy9dxs/24nhkaGzFv1LuaW5ty9fo93X3+fpMTc6UYcKtqrpiYrKTsnO5Z/uxgrG0tiImO4fe0eUwbMJCSw8IdZabMrGw+ib2RAj08mYmRhQvANb/4Yu5a0PHXPoqJ63TNzsGbi4dwpllpP60fraf3wv3iPnaNyTvSbjesOwOg/1OftPjR3E7fLaaoFj28PomtkQIvVEzGwNCXyujenR61Ra2dMnCuo7WtJ2mBNbX9Jtp2dkcmZsetwWTSKDr/MQ8/UkATfUC6/s4ngf3MfClahWU0azRuGnqkR8Q+CcJ2/Bb8958oybAVc+z6nve70yUQMLU0IveHNX2PWkp4njubOtmqjHkLcvDg6cwNt3x9Bm3nDifUL5eiMDWrTXJxZ8gtt5g2n08qJmNhakBgaze0d/3L1q30AmDnZUKNnCwBGH8stfwD7Rqwk8NK9stztEgv76yL61uZUmzMMQwdrEjwecfP11aQoy4OhvTVGeR6ol+Ifjvvrq6m9YgKV3uhFamg0not+Jvyf3DmUdQz1qfHhKIyq2pOZmELkievcnbmBjDwXg8yb1qT5vmWq97VXTAAgeOcp7r3zXRnv9ZNzU5ajLisnYmhhQsgNb/bnL0f52p5gNy8Oz9pAu3kjaDc3pxwdnqlejgwtTGj/wWuYOdqQGpvIg0NXuPDpbrIytOOi0ZO4roxRxzx17e98MTIrpK4dm7mBNu+PoPW84cT5hXJ8xgbC8sQozN2HI1O/ou2HI2n5zmDiH4Vzbtl2tYsNJvZWvLJ0DMa2OdPO3P/zHK7r9z2fHX8G5dU+abOyiglA5Q4Nsahky70iLo74HHHl1MIttJg5kI4rxhPtHczhaesJfoIRz+Xh0Mb9GBgZMP7jNzGxNMXnhhefjltBSp6Y2TjbkpUnZl3H9UbfUJ/ZG99XW9e+r3ax/6ucHzdNzE0YMX8M1o4VSIxNwPXwJfZ89huZL0D7dFfZj8l7rvbvE5yruSjP1c7lO1ezyXeu1iLPudql934gOysLq3qVqTH8VfQtTEkJiyHk/F3OTd+gtm1tdGljTt3rpexjB93wZqeGPra5gzWT8/Sx207rR9tp/fC7eI/flH1sUzsLBnw5HTN7K1LjkwjzeMSu8et4eE67L9oCuCrbo255jv17S3DsPzRrA+3njaD93OHE+IVyaOYGQvId+1/Jc+z3OnSF84Uc++sObAsKBR5a/pD4vP7euA8DI0MmfTINUwszvG94smrsMrX2yLainVrMDE0MeeOTaVRwqkBaShpB3oF8++6XXDp4XpWmx7g+ACz9Q/0i7fdzv+bMHu2/UPmfJw8aLlWK7JI+FUgIYJfTmPLOgtZL1/QAZcHImys0J3rJdWwyWXOil9wQPe1+ToE2qJomh/jihOtJe61JgzS5I0qTW4YG5Z0FracvTZFGcoorSsNVXe15loy26plmXN5Z0Hr++prTvOyM5Lim0RWFdk1lqY1+99tf3ll44ST/VX4jf4wHzS+3bZcVGSkghBBCCCGEEEIIIYQQQnv9R+f2Ly/yoGEhhBBCCCGEEEIIIYQQ4iUhFwWEEEIIIYQQQgghhBBCiJeETB8khBBCCCGEEEIIIYQQQnvJg4ZLlYwUEEIIIYQQQgghhBBCCCFeEjJSQAghhBBCCCGEEEIIIYT2kgcNlyoZKSCEEEIIIYQQQgghhBBCvCTkooAQQgghhBBCCCGEEEII8ZKQ6YOEEEIIIYQQQgghhBBCaC+ZPqhUyUgBIYQQQgghhBBCCCGEEOIlISMFhBBCCCGEEEIIIYQQQmiv7OzyzsF/iowUEEIIIYQQQgghhBBCCCFeEjJSQAghhBBCCCGEEEIIIYT2kmcKlCq5KCCeiAwt0eyaQUZ5Z0HrfdtkcnlnQeudcf+pvLOg9Xa5LC3vLGg9M+k0Fauhfnx5Z0HrBaSZlXcWtF6HbClHmujrSVukSUqanJZpkpmtKO8saL2+VgnlnQWtFxwu5UiT6mkyPYcmF/SNyzsLWm9CilF5Z0EIoYH8xiuEEEIIIYQQQgghhBBCvCTklhQhhBBCCCGEEEIIIYQQ2ktGwpcqGSkghBBCCCGE+D979x0eRdEAcPh3yV0uvVdSCaFD6F1670VQ6QgISFVUpPeiqJ8iKKIooFJFBEF6B+mhl/Tee+/JfX8kXHLJJbRUnPd57pHsze7OjDN7O7tTBEEQBEEQBEH4jxAjBQRBEARBEARBEARBEARBEISqSyFGCpQlMVJAEARBEARBEARBEARBEARBEP4jxEsBQRAEQRAEQRAEQRAEQRAEQfiPENMHCYIgCIIgCIIgCIIgCIIgCFWXWGi4TImRAoIgCIIgCIIgCIIgCIIgCILwHyFGCgiCIAiCIAiCIAiCIAiCIAhVl0JR2TF4rYiRAoIgCIIgCIIgCIIgCIIgCILwHyFeClSy7du3Y2xsXNnREARBEARBEARBEARBEARBqJpycyvv8xqq1tMHTZgwgR07dgAglUoxNTXF1dWVkSNHMmHCBDQ0/pvvPM6fP0/Xrl2Ji4ur0i8cao3vQd3p/dG2NCbRM4S7S38j+rpHieHN29Wj6fIxGNaxJS0iHo/vj+D76xnl94Z1bGk4bzgmrjXRs7fg7tLf8PrpuOox2taj7vv9MXGtiY61Cf+++z9Cj7uVWxrLS+8PhtN2ZDd0jfQJuOvNn0t+IcIruMTwbd/pRsthnbCuawdA8AM/jn6xh8B7Psowzq3r0XXKQOwa18TIypRfpnzJw5O3yj0t5WXS3PEMHj0AQyMDHt15wpeLNuDn6f9c+/YY1JVVm5dy4fhl5k9aonLMyR9NUAkbExnLgGZvlmHMq45bdx+wbdd+Hrt7ExUTy4Z1S+jeqX1lR6tCuX40DJfRXdEy0iPmjg83Fm4nwTOk1H3s+7WiybzhGDhakhQQyb3P/iDoeEFdajhzIA79WmHoYkNOeiZRt7y4s2YviT5h5Z2cV+I4oSfOMwYgtzQm2SOYR0t+Ja6Ua7Zpu/o0WDEG/bp2ZETE4bPpCIG/nlYb1mZIO5pvmU34sZu4Tfifcnvtj9+kzifDVcKmR8ZzpvH7ZZOocmY6ph/m7w1DamlKhmcgYat/IvXmI7VhpRYmWC+ahE4jF7ScahCz4zDhq34qEkgTi/dHYDKsO1JrMzJ8Q4j4fBvJF29XQGrKhv2EntScMVBZjtyX/ErcdfcSw5u0q0+9FWOV5chv02GCipQjqaEutRe8jVX/1siM9EgLjMJ9+W9En7mbd87xPXGY0AMdewsAkj2C8f7qANFn75ZXMsuU+bi+WE0diszShHTPQIJW/EzKjcdqw0otTbBb8i66jV2Q17Qh6pcjBK/4uVg4TUM9aswbg3Gftmga6ZMZFEHwqm0knqt+90SQV9cspgxDamlChmcgoat+IvVmCXlkYYLNoknoNK6VV9e2HyZs1dZi4czeHYTZmL7IaliQHZtI4rErhK/fgSIzq7yTUy4sxvfBZtoQZJYmpHkGEbjsZ5JvPFEbVmZpgv3SCei61kK7pg0Rv/xD0LJfSjy26aA3qLX5I+KOX8d70mfllYRyZzW+DzbvD0bL0oRUzyAClv5CUil55LhsPHr5eRT+81ECiuSRSd822M5+E20nGyQyTdL9wgj74W+i/7xQEckpF4ZvD8Rowgg0LUzJ8gkg5vPNpN9+qDasbvcOGL49EHldZyRaMjJ9Aoj7/jfSrqi/zuj16YLVFwtJOXuFiDnLyzEV5ct6Qm9spw/KK0ceQfgt3U7i9ZLKkTE1l49H39UZbWcbwrYexW/pdpUwOnXtcPjkHfSbOKNtb4nvkm2E/fRPBaSk/FiP702N6QV1zW/pNpJKySOnZRMK8ujno/gv3aYSxnJ0DyxHdEa3rgMAyfd9CVy3k+S73uWelvLU/sNhuI7qitxIj/A7Ppxesp2YUtohZnVs6TD3Tawa18TI3oKzK37j9s8nVMLYta5Lq2n9sWpcE30rEw5O/hrvk1X/t78y7h+dZw/Gql9r9GrXICc9k/ibnniu2kVKFW+zCcKLqPZPzfv06UNYWBj+/v4cO3aMrl27MmfOHAYMGEB2dnZlR08ogd2gtjRdOZYnGw5xqtcioq6703HnPHRszdSG17W3oOPvnxB13Z1TvRbh/u0hmq0ah23/VsowmjpyUgIiebBmD2kRcWqPI9WVE/84kDuLtpdHsipEt2mD6DypHweWbuPrQQtJiopn2u8Lketpl7hPrbYNuP33v3w/chXfDltKXGg0U39biJGViTKMlq42oU8COFDkJqs6GjP9HUZOGcFXi79lYv9pxETFsmH3F+jq6TxzX2tbK2YtfZ871+6p/d7H3Y/+TYcpP2O6Tyzr6FcZaWnp1HVxZuHc6ZUdlUrRYMYA6k3py81FOzjWbylpUfF03zMfaSl1zbyFCx1/mInf/sv803Mhfvsv03HLTMya1VKGsWpXH4/tpzg+YDmn3/kciaYm3XZ/iqaOvCKS9VJsBrelwapxeH9zkMs9FhB73YPWu+ejXcI1W8fBgla75hF73YPLPRbgveEQDdeMx7p/6+Jh7cypv2w0MVfVNwaT3IM43Wia8nOpy7wyTVt5MezfEevF7xH13T58Bswm5dYjHH9ZjqyGhdrwEi0Z2TGJRH23j/QnfmrDWH00FtORfQldsQWvXu8Tt+soDj8sQruBc3kmpcxYD25H/VXj8f3mL670mE/cdXdaPKMctdj1KXHX3bnSYz6+Gw5Sf80ErAqVI4lMk1b7FqFjb8HdSV9zqcNcHn70Ixlhscow6WExeKzezZVei7jSaxExlx/RfMfH6Oe/KK/KTAa+gd2ySYRv/AP3vh+SfOMxLr8uRVbDXG14jfxyFL7xD9Ie+6sNI5FJcdm1Ai07S3ynfc7jLtMJ+PQ7ssJjyjEl5ceo/xvYLJlM5Hf78O4/h5Sbj3Da9oy6FptAZCl1zXhwZ6w/HU/Ehj149phOyPyNGA14A+t548szKeXGdFAHHJZPJPTb/Tzq/RFJNx5T5/claJVQjiRaUrJjEgn7dj+pJZSjp7RsLbBfOp6ka+pfeFYXZoM64LjiXUK+/ZP7vT4i6foT6u1cjJZtSXVNSlZMIiEb/iwxj3LikwnZ8CcPB87nfvcPidpzllpfz8Soc9PyS0g50uvdGbNPpxH/0y5CRrxPutsDrDevQdNafV3TadGYtKtuhE9fTPDbM0i7cQ/rTSvRqlerWFipjSVmH79HmtuD8k5GuTIf3J6aKycQ/M0B7vb8hMTrT2iwa2HJ5UguIysmkaANB0h5FKA2jKaOnIzACAJW7ySzhDZudWI2qD1OK98leMOf3Ov1cV4e7VxUSl2TkRWbSPC3f5LyyF9tGKP2DYn+6zIPhy/jwcCFZIRE0WDPUrSsTcsxJeWr9fsDaDG5L2eW7GDngKWkRMUzYud8ZKW0Q2TachICo7j42V6SI+PVh9GVE/k4kDNLdpRTzMteZd0/mrSrT+C2k1zrt4RbI9YgkWrScu9CNHWrbptNEF5UtX8pIJfLsba2xtbWlubNm7Nw4UIOHTrEsWPH2L59OwAJCQlMmTIFS0tLDA0N6datG/fuFTzwW758OU2bNmXLli3Y29ujq6vLiBEjiI+PVznXtm3bqF+/Ptra2tSrV4/vv/9e+Z2/vz8SiYQDBw7QtWtXdHV1adKkCVevXlU5xvbt23FwcEBXV5ehQ4cSE1O8AXb48GFatGiBtrY2zs7OrFixQuUFh0QiYevWrQwdOhRdXV1q167N33//rYxH165dATAxMUEikTBhwgQA9u/fT+PGjdHR0cHMzIwePXqQkpLy0nn/KupM7Yvf7vP47TpPklco95b+TmpoDLXG91Abvta47qSGxHBv6e8keYXit+s8fnsuUHdaf2WYuHu+3F+1m6BD18jNVP9CKPzsPR59/gchR6tvD/hOE/ty+ruDPDhxk3DPYHZ99D1aOnKaD+5Q4j47P9jEld9PEfo4gEifUPbN/xGJRELtDo2UYdzP3+XYV/t4cOJmRSSjXL09eTjbv/2dC8cu4evhz6oPPkNbR5teQ9WXr6c0NDRYvmkRW7/cTmig+h4AOTk5xEbFKT/xsQnlkYQqoWO7VsyeMp6eXUouW6+z+pP78PDbQwQdu0WCRzBX5mxBqqNFzaElj5ao914fwi4+5NGmwyR6h/Fo02HCLz+m/nt9lGHOjl6P775LJHiGEP84kKsf/oi+nTlmrk4VkKqXU3Naf4J2nSNo5zmSvUJ5vORX0kNicJzQU214x3E9SA+O4fGSX0n2CiVo5zmCdp/HeXp/1YAaEpp+PwOvL/aTGhCp9li52TlkRCUoP5kxSWWdvHJhPmkIcX+cIm7fSTJ8gglf9RNZYdGYju6nNnxWSCThq34k/q+z5Calqg1jPKQrUZv3kXz+FllBEcTuPEbyxduYTx5ankkpM07T+hO86xzBO8+R4hWKe345ciihHNmP60l6cAzuS34lxSuU4J3nCN59jprTByjD2I3sisxEnzsTviL+pifpwdHE3/Ag6XGgMkzUydtEn7lLqm8Yqb5heK3bS3ZKOkYtapd7ml+V5XuDidl7mpg9p0j3DiZ4xc9khUZjMbav2vCZwZEEL99K7J/nyElSf49n9nYPpMb6+ExeS8otdzJDoki5+YS0J/7lmJLyYz55CHH7ThG3N6+uha3aml/X1OdRVkgkYSt/Iv7AOXJKqGu6zeuReusJCX9fICskkuRLd4g/fBEdV5fyTEq5sXpvENF7zhC9+zTp3sEELfuFzNAYLMf1URs+MziKwGU/E7P/PDmJ6vMIAA0NnDd9SMiXe8gIjCin2FcMmykDidp9hqhdp0n3DiEgP4+sxvVWGz4jOIqApb8QXUoeJV59RNzx66R7h5AREEH4z/+Q+iQAg9b1yzMp5cZo3JskHThO0oHjZPkFEbP+B7LDozB8e6Da8DHrfyBh2x9kPPIkOzCUuG+3kRUQgm6XtqoBNTSw/Gw+cd/9RnZw9e6BW2PqQCJ2nyVi1xnSvELwW7qdjJAYbMb3Uhs+IygKvyXbiPrjQonXo+S7Pviv/I3oQ/+SW01HKhVWY+pAInefJTI/j/yXbiMjNAbr8SXXNf8lv5SaR14zNhC+4wSpj/xJ8w7B5+MfQEOCUcfG5ZmUctV8Uh+ubzqE1/FbRHsGc2zuFqTaWtQfUnI7JPy+LxfW7sbj8DVyMtSXFb/z9/n3y/14Ha8+z0Mq6/7RbeRnhOy9QLJHMEmPA3kwZzM69hYYutYs9zQLpRDTB5Wpav9SQJ1u3brRpEkTDhw4gEKhoH///oSHh3P06FHc3Nxo3rw53bt3Jza24C2gt7c3+/bt4/Dhwxw/fpy7d+8yY8YM5fc//fQTixYtYs2aNTx58oS1a9eyZMkS5fRFTy1atIiPP/6Yu3fvUqdOHUaOHKl8oH/9+nUmTpzI9OnTuXv3Ll27dmX16tUq+584cYIxY8Ywe/ZsHj9+zJYtW9i+fTtr1qxRCbdixQreeust7t+/T79+/Rg9ejSxsbHY29vz559/AuDh4UFYWBgbNmwgLCyMkSNHMnHiRJ48ecL58+cZNmwYikpYuVsi08TEtSbhF1R7gkRceIB5S/UNdLOWtYkoEj78/H1MmtREItUst7hWNab2lhhamuBx6b5yW05mNj7Xn+DUos5zH0dLR46mTEpqfOW8FCpPNRxsMLcy48aFghudrMws7ly7R+OWDUvdd+KH44iPiefwnqMlhrGvacvfbn/w59VdrPx+CTUcbMos7kLVoe9ggY6VMWGFrju5mdlEXHMv8ToFYNHCRWUfgNDz90vdR2aoC0BGFa2PEpkmRq41iTp/X2V71IX7mLRUf90xblmbqAtFwp+7h1ETZ5Vrdu2P3iQzJomgXedLPL+eszXd731P15sbaLZlFjqOli+fmAoikUnRaeRC8qU7KtuTL91Bt3m9lz+ulgxFRqbKttyMTHRbNnjpY1YUiUwTQ9eaRBcpR9EX7mNcSjmKLlKOos/dx7BQObLs3YL4W540+GwiXR/+QIcLX+A8ZwhoSNRHREOC9ZB2eSMHb3m+crrKk0QmRbdxLRIv3lXZnnjxLnotX74cGfVsRYqbBw6rp9L49g7qn/4Wq5nDoRpOu1lqXWvx8g9eU24+RqdxLXSa5F27ZfZWGHRpSdLZ6vMQ5SmJTIqeay0SLtxV2Z544dXKEUCND98iOyaB6D1nnh24CnuaR/EXVEeJxl+4i8Er5lFhhm80RrtWDZKuq5/aqkqTSpE3qE3qFdXp6tKuuKHd9Dl/gyQSNPR0yU1QfblvMm00OXEJJP11vIQdqweJTIq+qzPx54uWo3sYtKpbSbGqWvLyqBbxRa5H8RfuYdCy7PJIQ0cLiVST7LjkMjtmRTJysEDf0hj/iwVtipzMbIKvu2NbDTo0lKUqc/8IyAzy2mxZ8dWzXAmCOtV6TYHS1KtXj/v373Pu3DkePHhAZGQkcnneMJ8vv/ySgwcPsn//fqZMmQJAeno6O3bswM4ubyj5xo0b6d+/P1999RXW1tasWrWKr776imHDhgFQs2ZN5UP78eMLhhJ//PHH9O+f1xNyxYoVNGzYEG9vb+rVq8eGDRvo3bs38+fPB6BOnTpcuXKF48cLboDWrFnD/Pnzlcd0dnZm1apVzJs3j2XLlinDTZgwgZEjRwKwdu1aNm7cyI0bN+jTpw+mpnnD5CwtLZVrCvj4+JCdnc2wYcNwdHQEoHHjynlzLjc1QEOqSUaUag/r9KgEtC2M1O6jbWFEepHwGVEJaMikyE0NSC9heNzrxtDCGICkInmRFJWAiZ36IZfq9P90JAnhsXj+W72H6KpjZplX/mOjVYfXxkbFYW1nVeJ+ri0bMXBkP8b1nFximEd3nrByzmcE+QZhamHChNlj+fHQJkZ1e5fEuMSySYBQJWhbGgMUu+6kRyWgV0pd07YwJj26yD7RCeiUcG0DaLl8NJHXPUjwKHldkMqkZWqIhlSTTDXXYLml+nTJLY2LXeMz86/ZWqYGZETGY9KqDvajunCp+4ISzx1/25t7MzeT4huGloURtT8YSvsjK7jY6ROyqnBDT9PEMK8xWuQ6lBMTh9Si+UsfN/nSbcwmDiHlxiMyA8LQ69AEwx5tQKPqvxwvqRxlRiUgz69vRcktjdWGL1yOdBwtMX2jIWEH/sVt1OfoOlvTYN1EJJoa+PzvgHI//fr2tP1nFRpyGTkp6dx+9ytSnrE+SGWTmuaXo6h4le1Z0fEYWpio3+k5yB2s0WpvSezBC3iPX4l2zRrYr56CRFOT8A17XzHWFaugrsWrbM+OjkeWf8/0MhKOXEJqZoTzvs+RSCRIZFJifjtK1A/7Xy3ClUBqaqA2j7Ki4zEsoe49D/2W9bAY2Z1HPee+WgSrgKd5lFU0j6ISkL1CHgFoGujS/PZPSLRkkJOL38IfSbioforKquxpXcuJKf67pmn2fNcjo/HDkehok3zionKbvGkDDIb1IXh49VgrqDSyp+WoyO9WVlQCWq9wPXqdSEvMo/gyzSPHRWPIDI8l/tL9ZweugvTy8yKlSJsiJToBwxKmWXpdVfb9Y2H1Vo4l9po7ye5Vs832n6F4PXvsV5bX9qWAQqFAIpHg5uZGcnIyZmaq842lpaXh41OwyKqDg4PyhQBAu3btyM3NxcPDA01NTYKCgpg0aRLvvfeeMkx2djZGRqoPRFxdXZX/trHJ60EcGRlJvXr1ePLkCUOHqg7xb9euncpLATc3N27evKkyMiAnJ4f09HRSU1PR1dUtdh49PT0MDAyIjFQ/9QJAkyZN6N69O40bN6Z379706tWL4cOHY2JS8k1cRkYGGRkZKtuyFDnIJGXz8KHYKAWJhFLHLRT9UiJRf5zXSPPBHRixtqDMbZ34OVA8zRIJ8Jz50HXqQJoP6sB376wku4RhhdVJr6E9+PTzggbpx+PyHjC+SB7p6umwbONC1n3yJQmlPNy/du6G8t8+7n48uPWY/Vd20m9Eb/b8+McrpEKobE5D29NmfcH6EOfGfpn3D3XXnWdVtWLfS0ose63Wjse4vj0nh6x6kehWiuJZUXK6Stghf7sCTT1tmn4/gwcf/URWbMnTAUWdLfTg5EkQ8be86HL9G+ze6oTflpJH9FQZL1AWnkfYyh+xXTuL2qc2gwIyA8OI238ak+GlT41WtRT97X/G73ixe4Wnm/O2SzQ0yIxO5OFHP0KugsT7fmhbmeA0Y6BKoy7FO5Qr3T5FaqSH9YDWuH47netDV1T5FwNAsTx4Zt17Fg0J2TEJBH76PeTmkvbAB5mVCVZTh1a7lwJK6u4pXyGL9No0wmLGW4Qu/YHUux7IHW2wWToFy6hYIjdWzzwqlh+vUI409LRx3vgB/p9sJjuuekzp9lzUXW9esZ2Rk5zG/Z4foamnjdEbrjgue5eMgAgSr1bXNRiK17Xnode3CybvjyVizjJyY+PzdtXVwXLdfKKWf0Nu/GvUuUZNOXqNm6sv5YWfA7yAGtMHYz7kDR69uQxFNWnr1h/Snp7rCtohByaob4dInqcd8tqqnPvHp+qvexeD+o5cG7Ss2HeCUJ29ti8Fnjx5Qs2aNcnNzcXGxobz588XC/O0F706kvwbHIlEQm7+3FE//fQTbdq0UQmnqan6gFwmkxU7xtP9n+fhdW5uLitWrFCOSChMW7tgUZnC5ykaT3U0NTU5deoUV65c4eTJk2zcuJFFixZx/fp1atZUPyfaunXrWLFihcq24XqNeMvAVW3455URm0Rudo6yJ+5T2uaGxXqWPpUelYB2kR6pcnNDcrOyyazCvUVf1aPTbgTe9Vb+ramV9//d0NKYpEI9B/XNjUiKVp93hXV5bwA9Zgxh8+g1hLkHPjN8dXD55L88vlMwDFumpQWAmYUpMZGFFgoyNyk2euApW6ca1HCw4Yvta5XbNPKHDl4KOM07ncYREhBabL/0tHR83H2xr2lbJmkRKk/wydtE3yl4UayplffzqG1pRFqhkUja5oaklXCdAkiPii824knb3JC06OKN3Zarx2HXqzknh64mtdCiVlVNZmwiudk5yIukS8vckIwo9Y34jMj4YqMItApdsw3q2qHrYEnL3z5Rfi/Jr3N9Q37nQvu5atcYyEnNIOlJEHrO1q+arHKVE5eIIjsHaZHe3JpmxsV6677QcWMTCZy2BomWDE0TQ7IjYrD6dAKZQVV/Lu+n5ahoT0Atc6NivbmeyoiMR8uyePjcrGzlSJGMiDhys3Mgt+AeK9krFG0rEyQyTRRZOQAosnJI9c/Lp8R7vhg2rYXTe3159MnWMkph2cuOzS9HlqrlSGpmVKxH84vIiozLy5dC943pXsHIrEyRyKQostSvy1QVlVTXpGZGr1TXrD4aQ/xf54jbexKADI8ANHS1sV07k8hN+6rVE77s2CQU2TnFRk7IzIyK9dZ9XnIna+QOVtTevrBgY/41vGXAfh50mklGQPjLRrnCFeSRajmSmb98HikpFGT45+VF6iN/dGrbUWPWsGr3UuBpXdM0U124VdPUuNjogaL0enfGYsVcIj5aTdq1gqm+ZPY2yOyssd64siBwfjmqeecYQQMnVqs1BrKelqMiv1sy81e7Zr9Onta1or/teXUt/pWPX2PaIOxmv8mjt1eQ+kT9ws1Vkfep24QVbofI89ohehZGpBRqh+iaGZL6HG3+10ll3z8C1F87AcveLbkxZLnKQsSC8Dp4LV8KnD17lgcPHvDhhx9iZ2dHeHg4UqkUJyenEvcJDAwkNDSUGjVqAHD16lU0NDSoU6cOVlZW2Nra4uvry+jRo186Xg0aNODatWsq24r+3bx5czw8PHBxefmFzLTyH4rm5OSobJdIJHTo0IEOHTqwdOlSHB0d+euvv5g7V/2w3wULFhT77kidKS8dr6cUWTnE3ffDqlMjQo8VzM1q1akxISfc1O4Tc8uLGr1Up1yw7tyYuHt+KLJz1O7zOshISScjJV1lW2JkHHXeaEzII38ANGWa1GpTnyOf7Sr1WF2nDKDHzGH8OH4twQ98yyvKFS41JY3UlDSVbdERMbTq1BLPR3kvVKQyKc3aNuH7tT+qPUaAdyCju72rsm3KvEno6evy9dKNRISqH4Uj05LhVNuRe9dfv2mY/muyU9JJLlLX0iLisenUiLiHeY0KDZkmVm3rcWdNyb1Eo9y8senUCPefCkaA2XRuTPQtL5VwrdaMw75PS04NX0NKUFQZpqTsKbJySLjvh0VnVyIKXbPNOzUmooRrdvwtLyyLXLMturiScM8XRXYOyd6hXOj8icr3dee/hVRfh0eLd5AWGqP2uBpaUvRr1yD2mvsrpqp8KbKySXvojf4bTUk6eVW5Xf+NpiSdvv7qx8/MIjsiBqSaGPZuT+LRy698zPKmyMoh8b4f5p0bE3msYEF7806NiTyhfp52deXIvIsrifnlCCDupic1hnZQ6fWsV8uG9PBYlQZdURKJBA0tWYnfVwWKrGxSH/hg2LEJCccL7hcNOjYl4eTLl6OUW08wGdxJJc/kzjXIjIitVi8EoHBda0biyYI80n+jKYmnXj6PNLTlxRaUU+Tk5vU0fNWRGhVMkZVNyn0fjDo1If54QZ4YdmpC/IkbpexZsnTvEB52m6OyzXbeKDT1dQhc+jOZodGvFOeKVjiP4grlkVGnJsS9ZB6VSEKVv/aolZ1NxmMvdNo1J/Xsv8rNOu2ak3Luaom76fXtgsXKj4j8dB1pl1TzMssviKChqm1L01kT0NDVIfrzzWSHV+37o6IUWdkk3/fFuLMrsccK0mrc2ZXY4zdL2fO/Iy+PfDDu1EQ1jzq5Envi1fKoxvuDsfvgTR6PXEXKPZ9n71CFZKWkE1+kHZIcGY9jx0ZEPipoh9i1qcfFz6rnaLWXVdn3j/XXvotVv1bcGLqStMDqdU16XSlyq889WHVQ7V8KZGRkEB4eTk5ODhERERw/fpx169YxYMAAxo0bh4aGBu3atWPIkCF8/vnn1K1bl9DQUI4ePcqQIUNo2bIlkNcLf/z48Xz55ZckJiYye/Zs3nrrLayt83ojLl++nNmzZ2NoaEjfvn3JyMjg1q1bxMXFlfhQvajZs2fTvn171q9fz5AhQzh58qTK1EEAS5cuZcCAAdjb2zNixAg0NDS4f/8+Dx48KLYocUkcHR2RSCQcOXKEfv36oaOjw6NHjzhz5gy9evXC0tKS69evExUVRf36JS/CJpfLleswPFVWUwd5bjlGm43vE3fPjxg3L5zHdEPX1gzfX/MWKmu08G10rE24OfsHAHx+PYPLxJ40WT4a353nMGtRm5oju3Bt+iblMSUyTQzr5E0BpSGTomNtglFDR7JT0knJ7x2oqStHv2ZBD1M9BwuMGjqSGZ9MWoj6h1BVzcVfjtFjxhCi/cOJ8gujx4yhZKZlcPtQwQ36yK+mkxgRyz/r9wB5Uwb1nfsWv8/ZSGxwFAb5PX4zUtLJTM2bIkpLV465U0HemNpbUqOBI6nxycSX8ICuqtq7dT/jZ40m2C+YIL9gxs8aQ3paOif/Oq0Ms3TDAqLCotj82VYyM7Lw9fBXOUZyYl4vgsLbZy2ZxuVTVwkPicDE3IR354xBT1+Xo3+cqIhkVbjU1DQCgwtGSISERuDu6YORoQE21lV/sddX9WTrcRrNGkSSbwSJfuE0mj2I7LRM/P66ogzTfsNUUsPjuLtuHwDuW0/Q68BiGswYQPAJN+x6t8CmY0NOFJoeqNXaCdQc2o7z735NVnK6cmRBVlIqOelVc5iz3w//0HTTDOLv+RJ/yxP7sd3RsTMncEdenaq76B20rU24N2szAAG/nsZxUi/qrxhD0O9nMW5ZB/tRXbkzbSMAuRlZxebjzEpIBVDZXn/ZaCJO3iYtJBq5uSEuHw5FaqBDyL6LVHXRPx/E7qu5pD3wJu32E0xG9kFWw4LYnXnTHll9Mh6plRkhH/9PuY92/byRexp62khNjdCuXxNFVjYZ3kEA6DSpg8zajLTHvsiszbGcMwqJhgZRW/6s+AS+BP8f/sF10wwSlOWoB9qFylGdRe8gtzblwazvAQj69RQOk3pRb8VYgn4/g3HLOtiN6sq9ad8qjxm0/RSOk3pTf814AraeQM/ZGuc5gwnYWnB/VXvhO0SfuUt6aAya+trYDGmPafsG3HpnXcVmwEuI/OkQjt98QOp9b1LcPDAb3RstW3Oif89LX41PxyKzNiPgw2+U++g0eFqOdJCaGaHTIK8cpXvllaOoX49jMWEAdismE7XtH+Q1bbCeOYKobUcqPH1lIXrrQez+N5e0B16k3nbH9Gld23UMAKtPxiGzNiP4o6+V+yjrmq76upZ45gbmk4aQ9siX1LueyJ1ssJo7msTTN4q9LKgOIn76m5ob5pByz4dkNw8sxvREy9acyN/y7l/s5o9BZmOK35yCuqXT0AkATT1tZKaG6DR0QpGZTbpXMIqMLNI8VEec5iSmABTbXl2E/XiYWt/OJuW+N0m3PLAa0wu5rTkRv+aNFrFfMBotazN8CuWRbn4eaehpIzMzRDc/j9K88n7HaswcRsp9H9L9w5FoSTHu1hzz4V3wW6C+k0pVl/Drn1ium0fmI0/S7z3GcER/pDaWJO3Lu3aYzJmI1NKMqEVfAHkvBCzXzCPm881k3HuiXHsgNyMDRXIqiswssrz9Vc6Rm5R3/110e3URuuUwtTfOIvmeL0m3PLAe0xO5rTnh+eXIceEotGzM8Jq1UbmPXuG6ZmaEXkMncrOySfPMK0cSmRTdQm1cuY0peg2dyElJJ92/+ozIeSovj2aTfM+HJDcPrPLz6Gldc1g4Gi1rU7xnF+SRrkoe5de1QnlUY/pgHOaNxHPGN2QERSlHRuWkpJObqvqwvbq4/fNx2swYRJxfBPF+4bSZOYjs9EyeHCxoh/T9eirJ4XFc+jyvHaIh08Ssdt4Idk0tKQZWplg0cCArJYP4gLznITJdOcZOBevsGdlbYNHAgfT4FJKqaJu/su4fG3w2EZthHbg9/kuyk9PQym+zZSelkltF22yC8KKq/UuB48ePY2Njg1QqxcTEhCZNmvDtt98yfvx4NDQ0ADh69CiLFi1i4sSJREVFYW1tTadOnbCyKrgYuri4MGzYMPr160dsbCz9+vXj+++/V34/efJkdHV1+eKLL5g3bx56eno0btyYDz744Lnj2rZtW7Zu3cqyZctYvnw5PXr0YPHixaxaVfCwqHfv3hw5coSVK1eyfv16ZDIZ9erVY/Lkkhc/LcrW1pYVK1Ywf/583n33XcaNG8enn37KxYsX+eabb0hMTMTR0ZGvvvqKvn37Pvdxy1Lw39eQm+jTYO5QtC2NSfQI5tKYL0gNzutZpGNpjK5twToQqUFRXBrzBU1XjKHWhJ6kR8RxZ8mvhPxT8LZYx8qEXqcLpn+pO30AdacPIPLKYy68mbdGg2kTZ7ocWKwM03TFWAD8917k5gdbyjXNZeXsD38j09bizVUT0THSI/CuN1vGrlUZUWBia64yXVWHsb2QymVM+EH1BdaJb/Zz4pu8RfPsXWsxY89S5XdDlowD4Mb+C+z5eHN5JqnM/f79HuTacj5e+wEGRgY8vvOED0Z9ojKiwKqGZalTbqljYWPBiu8WY2xqRHxMPA9vP2HywBmEh1T9qTtexkN3LybO+lT59/qNeY3YwX17sGbxR5UVrQrz+LsjSLW1aL1uAlpGukTf8eHMyM/JLlTX9GzNVXorRN/y4vL7m2jy6QiafDKc5IAILk3bREyhIcF1J+TN/96r0LUI4MoHW/Ddd6mcU/Vywg5dQ8vEgNpzhyG3MibZPYiboz4nLf+aLbc0RqfQwmdpgVHcHLWeBivH4vhuLzIi4ni0aAfh/7xYj0vtGqY0+2EWWqYGZMYkEufmxZV+S5XnrcoS/7lEuIkBlrPeQWphSoZnAAETl5MVmtfLSGphglYNC5V9XP4paADrNK6N8eAuZAZH4NlpEgASuRaWc8ei5WBNbkoaSefdCJ77FblJKRWWrlcRfugqMhN9XOa+idzKmCT3INxGfUa6shyZFCtHbqM+p97KcTi824v0iDieLNpORKFylB4aw62311Jv5Tg6nPucjPA4An46ju/GQ8owcgsjXDfNQG5lTFZSKkmPA7n1zjpiLlb9UV5xhy+jaWKA9Zy3kVmaku4RgM/4lWSG5JUjmZUJWkUWHax/4hvlv/VcXTAd2pmMoAgetc/rkZsVFo336GXYLZuE+ckNZEXEEPnLYSK+V7+wXlWX8M9lNE0MsZxdUNf8J64g62keWZoiK1LXah8t9GDXtTbGQ/LqmkfHvPvtyE17QaHA6qMxyKzNyI5JJOnsDcK/+K3iElaGYv/+F00TA2p8+BYySxPSPALxHLtatRwVyaNGJwteoug1ccFsWGcygiK533Zqhca9osT8/S9SEwPs8vMo1SMQ9zFrlHmkZWmCvEhdcz1V8FJXv4kL5sM6kREUyZ020wDQ0JXjtPY95DZm5KZnkuYTgs+sDcT8/S/VUcqJC8QYG2I8bTRSC1MyvQMIn76Y7LC8EbVSC1OkNgWdRgxH9Ecik2K+eBbmi2cptycdOknU4i8rPP4VIfrQFaQmBtjPHY6WpQmp7oE8Hr2WjPzfOZlV8XLU9ExBXug3qYXFmx1JD4rErdV0ALSsTVTC2E4fjO30wSRcecTDYdVvfvOYv68gMzHAbu6IvDzyCOTJmLVkBJdc15qe/kr5b/0mLlgM60R6UCS3W+ctUG09oQ8achn1tqqOQg36ci9BX+0r5xSVjxub89ohPdZMQNtQl7C7Puwf/TlZhdohhjVU2yH6ViaMP17wPKTVtP60mtafoKtP2Pt23vMQa1dn3t63SBmm67IxADz84yLHP6qaLywr6/7R4d1eALQ5qFrPHszeTMjeC+WZZKE01bBzRlUmUbzOq7Q+p+XLl3Pw4EHu3r1b2VGp8v6wefnpk/4rrspf3+mMysrVrNfzIXpZunjv58qOQpW313XpswP9x5nkiOtRaRz1XqMFMstJcIp+ZUehyrPUSnt2oP84mVQ04J4lPbPa99UqdzmK51vU9r/M0uz1XWutrIRFGVZ2FKo8ieQ//4joma7IdCo7ClVeo4zqNSVhZegTsaeyo1DtpP4w59mByonutA2Vdu7yIu4+BUEQBEEQBEEQBEEQBEEQhKpLITqalCWNyo6AIAiCIAiCIAiCIAiCIAiCIAgVQ7wUIG/6IDF1kCAIgiAIgiAIgiAIgiAIgvC6E9MHCYIgCIIgCIIgCIIgCIIgCFVXrljzpCyJkQKCIAiCIAiCIAiCIAiCIAiC8B8hRgoIgiAIgiAIgiAIgiAIgiAIVVeuWGi4LImRAoIgCIIgCIIgCIIgCIIgCILwHyFeCgiCIAiCIAiCIAiCIAiCIAjCf4R4KSAIgiAIgiAIgiAIgiAIgiBUXbm5lfd5Cd9//z01a9ZEW1ubFi1acOnSpVLDZ2RksGjRIhwdHZHL5dSqVYtffvnlpc79PMSaAoIgCIIgCIIgCIIgCIIgCIJQBvbu3csHH3zA999/T4cOHdiyZQt9+/bl8ePHODg4qN3nrbfeIiIigp9//hkXFxciIyPJzs4utziKlwKCIAiCIAiCIAiCIAiCIAhC1aVQVHYMntv//vc/Jk2axOTJkwH45ptvOHHiBJs3b2bdunXFwh8/fpwLFy7g6+uLqakpAE5OTuUaRzF9kCAIgiAIgiAIgiAIgiAIgiCokZGRQWJiosonIyNDbdjMzEzc3Nzo1auXyvZevXpx5coVtfv8/ffftGzZkvXr12Nra0udOnX4+OOPSUtLK/O0PCVeCgiCIAiCIAiCIAiCIAiCIAiCGuvWrcPIyEjlo67HP0B0dDQ5OTlYWVmpbLeysiI8PFztPr6+vly+fJmHDx/y119/8c0337B//35mzJhR5ml5SkwfJLyQTImksqNQ5b2ZXn7zfb0urOW2lR2FKm+v69LKjkKV9/b9lZUdhSrPo/Xsyo5ClXY5y7iyo1DlxWlXdgyqPocsg8qOQpWXVNkRqAasc15uATtBKCwlUlbZUajybmiLPHqW+hnievQs04YnVnYUqrycqJTKjoLwOnrJBX/LwoIFC5g7d67KNrlcXuo+kiLPUBUKRbFtT+Xm5iKRSNi5cydGRkZA3hREw4cP57vvvkNHR+cVYq+eeCkgCIIgCIIgCIIgCIIgCIIgCGrI5fJnvgR4ytzcHE1NzWKjAiIjI4uNHnjKxsYGW1tb5QsBgPr166NQKAgODqZ27dovH/kSiOmDBEEQBEEQBEEQBEEQBEEQhKorV1F5nxegpaVFixYtOHXqlMr2U6dO0b59e7X7dOjQgdDQUJKTk5XbPD090dDQwM7O7sXz6jmIlwKCIAiCIAiCIAiCIAiCIAiCUAbmzp3L1q1b+eWXX3jy5AkffvghgYGBTJs2DcibjmjcuHHK8KNGjcLMzIx3332Xx48fc/HiRT755BMmTpxYLlMHgZg+SBAEQRAEQRAEQRAEQRAEQajKFNVnzZO3336bmJgYVq5cSVhYGI0aNeLo0aM4OjoCEBYWRmBgoDK8vr4+p06dYtasWbRs2RIzMzPeeustVq9eXW5xFC8FBEEQBEEQBEEQBEEQBEEQBKGMTJ8+nenTp6v9bvv27cW21atXr9iUQ+VJTB8kCIIgCIIgCIIgCIIgCIIgCP8RYqSAIAiCIAiCIAiCIAiCIAiCUHW94IK/QunESAFBEARBEARBEARBEARBEARB+I8QIwUEQRAEQRAEQRAEQRAEQRCEKkuRW30WGq4OxEgBQRAEQRAEQRAEQRAEQRAEQfiPEC8FBEEQBEEQBEEQBEEQBEEQBOE/4j/3UkAikXDw4MFSw0yYMIEhQ4a80HGdnJz45ptvXjpeRXXp0oUPPvigzI4nCIIgCIIgCIIgCIIgCIJQLeUqKu/zGqryawpMmDCBHTt2MHXqVH744QeV76ZPn87mzZsZP34827dvf+Fj+/v7U7NmTe7cuUPTpk2V2zds2IBCUb7/w7dv384HH3xAfHy82u8PHDiATCYr1zhUhsYfDcNldFe0jPSIuePDzYXbSfAMKXUf+36taDJvOPqOliQHRHL3sz8IPn5LJUzt8T1o8H4/dCyNifcMwW3p70Td8FB+r21uSNNF72DTuTFaRrpEXvPg1uIdJPlFKMP02L8Iq/b1VY7rf+gq/77/XRmkvOxYT+iN7fRBaFmakOoRhN/S7SRef6I2rMzSmJrLx6Pv6oy2sw1hW4/it3S7ShidunY4fPIO+k2c0ba3xHfJNsJ++qcCUlK+OnwwjCajuiI30iPsjg+nlmwnxqvksmZW25Y3PnoT60Y1MbK34MyK33D75YRKmDbTB1KnTyvMatmQlZ5JqJsXFz7bS6xvWHknp1y4FqmPN16gPho4WpIUEMm9z/4gqFB9bDhzIA79WmHoYkNOeiZRt7y4s2YviT7VM4+e5dbdB2zbtZ/H7t5ExcSyYd0SundqX9nRqhCmY/ph/t4wpJamZHgGErb6J1JvPlIbVmphgvWiSeg0ckHLqQYxOw4TvuqnIoE0sXh/BCbDuiO1NiPDN4SIz7eRfPF2BaSm7LT8cBj1R+ddeyLv+HBp8XbinlGvavZtRauPh2PkaElCQCQ3vvgD/yK/c3rWJrRZ8A4OXV3R1NYiwTec85/8RPQDfwCmBf2u9thXV+/m3paqf01/44NhNB3VFW0jPULv+HByyXaiS7lmm9e2pWP+NdvY3oLTK37jZpFrdrMx3Wk+pjtGdhYARHsFc3nDX/iev1+uaSkL5XG/ZNmmLvWn98e0cU10rU24MPFrgo+7FTuv4+C26NUwJSczh9gHftz77A9i7viUSzpfhahrL6bmhB64TB+AtqUxSR4hPFj6KzHXPUoMb9auHo2Xj8Wgri3pEfF4fXcY/1/PKL+36deKOnMGo+9khUSmSYpvON4/HCVo/+WKSE6ZKOs8cRzdFfsRHTGsZw9A/H0/Hq/bS3yh+iPR1KDex29i92YHtC2MSY+MJ3DvBTy+Pgjl3P58GbYTeuE4YyBalsakeATjtWQH8dfdSwxv3K4+tVeMQ6+uHZkRcQRs+puQX0+rhLGf0g/b8T3RtjUnKzaRyCPX8Vmzm9yMrLxzju+J7YSe6NjnXbtTPILx++pPYs7eLbd0ljXxm1agMuoZgLa1CQ0Xj8SqWxM0tLVI8Q3j9tyfSLjvVz4JLWPS9n3R6joMiaEJueGBZBzcSq7f42fup+FUH50Za8kNDyDtqw8KfaGJrMdwZC27ITEyIzcqhMwjO8hxr1732YVpdR+EvP9beekJ8Sft9+/J8XzwzP00azdEb9HX5Ab7kbx4qnK7hq0j2m9OQNOpDhoW1qT9/h2ZJw6UZxIEodJVi5EC9vb27Nmzh7S0NOW29PR0du/ejYODQ5mfz8jICGNj4zI/7oswNTXFwMCgUuNQ1hrMGED9KX25tWgHx/stJS0qnm575iPV0y5xH/MWLrzxw0z89l/maM+F+O2/TMctMzFrVksZxnFQG1qsGMPDb//maK/FRF33oOvOT9C1NVOG6fTLhxg4WnLh3a852msxKcHRdN+7AE0ducr5vH4/y59NZig/N+b9UvYZ8QrMB7en5soJBH9zgLs9PyHx+hMa7FqIlq252vAachlZMYkEbThAyqMAtWE0deRkBEYQsHonmRFx5Rn9CtN62gBaTu7LqaU7+G3gUlKi4nl753y0SilrMh05CYFRXPh8L8mR8WrD2Lepz51fT/HbkOXsG/M5GlJNRvz2KbIi5ag6aDBjAPWm9OXmoh0cy6+P3Z+jPnbMr4//lFAfrdrVx2P7KY4PWM7pdz5HoqlJt92fFqtrr4u0tHTqujizcO70yo5KhTLs3xHrxe8R9d0+fAbMJuXWIxx/WY6shoXa8BItGdkxiUR9t4/0J+obY1YfjcV0ZF9CV2zBq9f7xO06isMPi9Bu4FyeSSlTTd8fgOt7fbm8eAd/DlhKalQ8A3bNR1ZKvbJq7kLP72fideAyf/ReiNeBy/T8fiaWTQvqlZaRLkMOLCU3O4ej475gX7dPubpqF5mJqcowO5rPUPmc++hHFLm5+B67Ua5pLgttpw2g9eS+nFy6g+351+x3nuOaHR8YxflSrtlJYbGc/3wv2wcuYfvAJfhfeczwn+ZiXtu2nFJSNsrrfkmqKyf+USC3Fu0o8ThJvmHcWrSDf7ot4NSQlaQERdNt96fITavWPamoay/GdnBbGq8ch+c3BznXcyEx191pt+tTdArdKxem62BBu53ziLnuzrmeC/HccBDX1eOp0b+VMkxWfDKe3xzkwoBlnO06n4A9F2n2zVQsu7hWVLJeSXnkiXn7BoQcvMK/b67m4oBlpIVE02HPfLStTZRhas8ciNO4HtxfuJ0znT7m0apduEwfgPOk3uWe5hdlObgddVaNx/+bv7jRYz7x191psnsB8hLySNvBgqa78sLd6DEf/w0HqbPmXSz6t1aGsXrzDWotGonfV/u51nEuTz7cgtXgdtRaNFIZJiMsBp/Vu7jRayE3ei0k9vJDXHd8gl5du3JPc1kQv2kFKqueyYz06HR4ObnZOVwZvZ6znT/hwfKdZCWklHuay4K06RvIh0wm8/Q+Ur/6gBy/x+hMWYbEWH2bX0lbF+1RH5Djda/YV1r9xiBr14eMv34k9fMZZF05jva7C9CwrT732YXJ2nRBe8x00g/tInnJVLI9HqD3yTokZpal76ijh87U+WQ/Kv4yRKKlTW5kGOn7tpIbH1NOMRdemSK38j6voWrxUqB58+Y4ODhw4EDBW7oDBw5gb29Ps2bNlNvUTeHTtGlTli9frva4NWvWBKBZs2ZIJBK6dOkCFJ8+qEuXLsycOZOZM2dibGyMmZkZixcvLnU0QUJCAlOmTMHS0hJDQ0O6devGvXvFL84lKTp9kJOTE2vXrmXixIkYGBjg4ODAjz/+qLJPSEgIb7/9NiYmJpiZmTF48GD8/f2V358/f57WrVujp6eHsbExHTp0ICBA/YPi8lBvch8efnuIoGO3SPAI5uqcLUh1tHAaWnKv2nrv9SH84kMebTpMoncYjzYdJvzyY+q916cgzJS++Ow+j8+u8yR6h+K27HdSQ2OoM647AAbO1li0rM2N+duIvedLkk8YNxdsQ6orx2loO5Xz5aRlkh6VoPxkJaVRldSYOpCI3WeJ2HWGNK8Q/JZuJyMkBpvxvdSGzwiKwm/JNqL+uEBOUqraMMl3ffBf+RvRh/4lNzOrPKNfYVpO6sPVTYfwOn6LaM9gjn60Bam2FvUHl1zWwu/7cn7tbtwPXyMnQ30+7B+/nof7LxHjFULUk0COfvwjRnbmWDV2KqeUlJ/6Rerjlfz6WPMZ9TFMTX2sX6g+nh29Ht99l0jwDCH+cSBXP/wRfTtzzFydKiBVFa9ju1bMnjKenl06VHZUKpT5pCHE/XGKuH0nyfAJJnzVT2SFRWM6up/a8FkhkYSv+pH4v86SW8K1yHhIV6I27yP5/C2ygiKI3XmM5Iu3MZ88tDyTUqYaT+rD7Y2H8Dt+iziPYM5+mHftcRlScr1qPLkPwZcecue7w8T7hHHnu8OE/PuYxpML6lWz9weSHBbL+Y9+JPKuL0nB0YT8+4jEgEhlmLSoBJWPU6/mhFx5QlJgVLmmuSy0mtSHK5sO4Zl/zT7y0RZk2lo0KOWaHXbfl3Nrd/Pk8DWyS7hme5+5g8+5e8T6hRPrF87FL/4gMzWdGs1dyispZaK87pdCz93n3vr9BB27VeJx/P+6SvilRyQHRpHgGYLb8p1oGepi3KDsO+G8ClHXXkytqf0I2H2egF3nSfYK5cHS30gLiaHm+B5qwzuN605acAwPlv5GslcoAbvOE7D7PC7vD1CGib7yhLBjt0j2CiU1IBLfrcdJfByIWeu6FZWsV1IeeeI24zv8tp8m4VEAyd6h3PnoJ9CQYNGxkTKMacvahJ+4RcTpu6QGRRN65AZR5x9g0qRmuaf5RTlM60/orrOE7jxLqlcIXkt2kBESg90E9e0O23E9SQ+OwWvJDlK9QgjdeZbQ3edwnD5QGcaoRW0SbnoQceBf0oOiiL1wn/C/rmDYpODBZPTJ28ScuUuabxhpvmH4rttLTko6hi1ql3uay4L4TStQWfWs9syBpIbEcOeDLcTf8SE1KJroy49ILXQtr8pknQeTff002ddPoYgMJvPgVhTx0cg6qL/Pfko+YjrZty+SG1B8JIa0RReyTv9BzhM3FLERZF85Ro77HWRdhpRTKsqXVt/hZF44RtaFo+SGBpK+83tyYyLR6j6w1P10Jn5I1tUz5HgXH3WR4+dB+p4fybp2DrJej+cigvAs1eKlAMC7777Ltm3blH//8ssvTJw48ZWOeeNGXo+e06dPExYWpvLSoagdO3YglUq5fv063377LV9//TVbt25VG1ahUNC/f3/Cw8M5evQobm5uNG/enO7duxMbG/vS8f3qq69o2bIld+7cYfr06bz//vu4u+cN30xNTaVr167o6+tz8eJFLl++jL6+Pn369CEzM5Ps7GyGDBlC586duX//PlevXmXKlClIJJKXjs+L0HewQMfKmLALBcO5cjOzibjmjkXLkm/wzFu4qOwDEHb+vnIfDZkmpq41CbvwUDXMhYeY54fR1MqbJavwg15FroLcrBwsWtVR2c9pWHvefLiZ/uc+o9nSkaX2yqtoEpkUfVdn4s+rvlyKv3APg1bVowFWEYzsLdC3NMb/UkG5ycnMJui6O7Zl3JiQG+gCkB5fPXqdPFVafTQvpT5aqKmPoefvl7qPzDAvjzKqWR4JJZPIpOg0ciH50h2V7cmX7qDbvN7LH1dLhiIjU2VbbkYmui0bvPQxK5KBgwV6VsYEXVStV6HX3bEu5dpj1dyF4Iuq9Sr4wn2VfRx7Nifqvi89N89i/J3vGH5sNfVHdinxmDrmhjh0a4r73vMvnZ6KYpx/zfYrcs0OvO6OXRlesyUaEuoPbItMR07Iba8yO25ZK6/7pZehIdOk9piuZCakEP+44jqRPIuoay9GItPE2LUmkUWmGIm88ADTIvfBT5m2qE1kkfIUef4+xk1qIpFqqt3H/I2G6LvYEH1N/ZSWVUlF5YlUR46GVEpmfLJyW8x1Dyw6NkLP2RoAwwYOmLapS/iZu6+QorInkWli4OpMbJE8ir1wD6OW6vPIqGUdYi+otlNiz93DoImzMo8Sbnhg4OqMYf4oJm1HS8y7NyP69J1ixwNAQ4LVkPZo6spJvOX5iqkqf+I3rUBl1jPr3s2Jv+dLq5/m0PfhZrqcWovj6K6vmKIKoilFw86FbE/VOpHtcQdNp5Lvs6WtuqNhZkPmyd1qv5dIZSiyVR90K7Iy0axZX234Kk1TiqZTHbIfqHZyyH7ohrR2wxJ3k3XsjYalDRl//VreMRTKk1hToExV+TUFnho7diwLFizA398fiUTCv//+y549ezh//vxLH9PCIm+aAzMzM6ytrUsNa29vz9dff41EIqFu3bo8ePCAr7/+mvfee69Y2HPnzvHgwQMiIyORy/OmzPjyyy85ePAg+/fvZ8qUKS8V3379+jF9et4UFZ9++ilff/0158+fp169euzZswcNDQ22bt2qfNC/bds2jI2NOX/+PC1btiQhIYEBAwZQq1beTVj9+qX/AGRkZJCRkaGyLUuRg0yi/ge5NNqWxgCkRyWobE+PSkDPruRhcNoWxqRHF9knOgFtCyMA5KYGaEg1i4eJSkAn/5wJ3mEkB0XRdMHb3Pj0Z7JTM6g3tR86VsboWBkr9/E78C8pQVGkRSZgXM+OpgvewqSBA2ff+fyF01seZKYGSKSaZBXJw6yoBLQsjCsnUlWQXv7/99Qi+ZQanYBhCdMsvaxuS0YTdMODaM/gMj1ueSvr+qiTXx/Vabl8NJHXPUjwqF55JJRM08QQiVST7GjV6cZyYuKQWjR/6eMmX7qN2cQhpNx4RGZAGHodmmDYow1ovPhvTmXQzb8OpxWpI2lRCRiUUq90LYxJjS5+vdItVK8MHSxoMKY797ce5/amv7FsWosOK8eRk5mN55/F5++uO7wjWSnp+JXSI7yqeHrNTilyPUqJTsCoDK7ZFnXtGPfXcqRyGZkp6RyY+g0xXqGvfNzyUl73Sy/CtkdTOmyeiVRHi7SIeM688zkZscnP3rGCiLr2Yp7eK2cUKVMZUQnISygf2pbGREbdLxZeQyZFy9SAjPzpTaQGOvS5+x0aWlIUObncW7CNqIsP1RyxainPPCmsweJ3SAuPVckTr02HkRnq0uPylyhycpFoavB43T5CDl599YSVIZmpIRpSTTLV5JFp/nWqKLmlETFFwmfm55HM1IDMyHgiDl5BZmZIi79XggQ0ZFKCt50kYOMhlf306tvT8p/VaMhl5KSkc//dL0l5xpohVYH4TStQmfVMz8GSmuN74L3lGJ4bDmLSrBauq8eTm5lN0B+XXj1x5UiiZ4hEUxNFUrzKdkVSAhIDY/X7mNugNWA8aZvmQ676KU6yPe4g6zyYHJ+HKGLC0azdBGmjNqBRbfoJK0kMjPLyKFG1LaJIiENiZKp2Hw0rW7Tffo+U1R+UmEeC8F9UbV4KmJub079/f3bs2KHsiW9uXrYP+ErTtm1blV717dq146uvviInJwdNTdUHFm5ubiQnJ2NmpjpXXlpaGj4+L79Qm6trwRydEokEa2trIiMjlef09vYutg5Beno6Pj4+9OrViwkTJtC7d2969uxJjx49eOutt7CxsSnxfOvWrWPFihUq24bqN+ZNg2fPFeo0tD2t1xeM5Dg/9kug+PpZEokEnvHCrfgsTZLiG4sdGOX0TorsHC5N3kCb/73HiCc/kpudQ/ilR4QU6ZHjs+u88t8JHsEk+YbT98RqTBo7EZe/uFyVoDatlROVqqDBkPb0WltQ1v58N7+sFQ0okZRpPvVYNR6LevbsHL6q7A5aTpyGtqdNofp4Lr8+Fsuk56iPajK2xALYau14jOvbc3JI1c8j4SW8QFl4HmErf8R27Sxqn9oMCsgMDCNu/2lMhqsfYl7Zag9pT6fPCurV0Qnq65Xkea49xS7rEpUpCiUaGkTd9+XG5/sAiHkUgGkdWxqM7a7+QeXbnfH660qJU6FVpoZD2tOn0DV7XwnX7Oe5P3geMb5h/NJ3EXJDXer2bcWAr6by+9urq8xDlAq/X3oO4f8+4WjPRchN9XEZ3ZWOW2ZyvP9yMmISX/hYZUHUtbJRLG8klFqmik2T+rQdVGh7dnI657ovQKqnjUXHhjRePobUgEiir1T90QJQPnnylMuMAdgNac/lYauUC+gC2A5uh92bb3Dr/e9I8gjGqJEjjVeOJT0ijqB9Ve9hpaJIhuRdi0rLpCJ/F8kj4/YNcPpgKB7zfybhthe6TtbUWT2BjIg4/L8uGLWf6h3KjW7zkBrpYTmgDQ2+ncHtocur3IsB8Zv2bJVRzyQaGsTd8+XJur0AJDwMwKCuHTXH96jyLwWU1LT51eabRAPtMR+TeXwXiqiSy0HGXz+h/dZMdOd/DwpQxISRdeM0stZV8z77uagtW2oySaKBzvSFZBzYTm646KgmCIVVm5cCABMnTmTmzJkAfPfdd8W+19DQKPYjklUJc4Hl5uZiY2OjdhTDqyxgLJPJVP6WSCTk5r/lzM3NpUWLFuzcubPYfk9HRGzbto3Zs2dz/Phx9u7dy+LFizl16hRt27ZVe74FCxYwd+5clW0H6k5VG7ao4JO3ib5T8ALk6RQ+OpZGpBd6wy83NyzWG66w9Kj4Yr2Qtc0NSY/Oa5hmxCaRm52DdpGe8trmRirHjX3gz7Gei5AZ6KAhk5IRm0TvI8uJva9+wcun++RkZmNY06pKvBTIik1CkZ2DrEjvHJm5EVnR8ZUSp6rA+9RtQtWUNT0LI1IKlTVdM8NiPQRfVvcV43Dp0Zzdb60mOfzlpwSrKCXVR21LI9IK5ZG2uSFpz6iPRXudapsbkhZd/EFRy9XjsOvVnJNDV5MaVvXzSHh+OXGJKLJzkFqYqGzXNDMm+xWuRTmxiQROW4NES4amiSHZETFYfTqBzKCIV4xx+fA/dZuIu2p+5yyMSH2BepUaFa/SUxnypiQpXK9SI+OJK9Lgj/MOxblfK4qybl0XE5canJ6+6YXSU1G8Srhm66u5ZqeUwTU7NyuHuIC8MhT+wA+bJs60ercPxxf+8srHLgsVdb/0InLSMkj2jyDZP4KY2z4MvPwlLiM782jT4Rc+VlkQde3VKO+VLVXTLjc3IqOEOpYeGa8ctVIQ3pDcrGwy4wqNGlEoSPHPq18JjwLQr21L7VmDq/xLgXLNE8Dl/f7UnT2Yf99aS+KTIJXvGi4dhdemvwk5lDcyINE9CB07c+rMGlylXgpkxSaSm52DvEgbS8vcsNjogacyIhOQF8lTrfw8ysrPo1qfvkX4HxcJ3XkWgJQnQWjqyqn35RT8v/lL+UBPkZVDWn7ZSrrni2HTWti/1w/3T34qy2S+MvGbVrLKrGfpkXEkFXmBlOQVSo1Ci15XVYqURBQ5OUgMVe+zJfpGKJLji+8g10HToTYats7Ih+U/q5FIkGhooPfFX6RvWUaO931ISSR921qQypDoGaBIiEVrwHhyY6vmfXZpFEkJeXlkVCSPDE2KjR4AQEcHqXM9NB1roz1udn7gvDwy3H6SlPXzyHl8t/wjLpQNMdKjTFWrsUJP58fPzMykd+/exb63sLAgLCxM+XdiYiJ+fiU/9NXS0gIgJyfnmee+du1asb9r165dbJQA5C2MHB4ejlQqxcXFReVTXqMbmjdvjpeXF5aWlsXOaWRU8EPcrFkzFixYwJUrV2jUqBG7du0q8ZhyuRxDQ0OVz/NOHZSdkq5sUCb7R5DgGUJaRDw2nQoWANKQaWLVth5Rt0qeBzHazRvrQvsA2HRurNwnNyuH2Pt+KscFsOnUiGg1x81KSiMjNgmDmlaYNnEm6IRbiec2qmuHppaUtIj450lyuVNkZZN83xfjzqojNYw7u5J0s/hiQv8VmSnpxAdEKD8xXiEkR8bj9IZqWbNvU48Qt1efc7PHynHU6dOSvSPXkhBUdRcVLOxF6qO6evNUlJt38brWuXGxfVqtGYdD35acHrGWlGqSR8LzU2Rlk/bQG/03mqps13+jKam33V/9+JlZZEfEgFQTw97tSTp9/ZWPWR6yUtJJ9I9QfuI8Q0iJiMe+o2q9qtGmHuGlXHsibntj11G1Xtl1aqyyT/gtT4xrqY7sM3a2Jik4utjx6r/Tmcj7vsQ8CXzZpJWrzJR04gIilJ/oEq7ZDm3qEVwG1+yiJBKJ8qFNVVBR90uvQiKRoCGXPTtgORF17dUosnKIv++HRefGKtstOjci9qb6Odpj3byw6KyaV5ZdXIm/54ciu+R2k0QCmvKqU79KUp554jJ9AHU/HMqVkZ8Tf694O1Sqo4WiyLzEipxcJBoVs87b81Jk5ZB03xfTIu0O006uJJQwt3/CLU9MOxUJ38WVpHu+yjzS0JEXT39ubl7hKS0LJCCpQtfup8RvWskqs57F3PBEv8i1XN/ZmlQ11/IqJyeb3GBvpHWaqmyW1mlKjr+a++yMVFLXzyTtqznKT/bV4+RGBJP21RxyAos8J8jOQpEQCxqaSF3bk/Owat5nlyonmxx/T6SNWqhsljZqQbbXo+Lh01JJWjCJ5MVTlJ/Ms4fJCQ0kefEUcnxevf0iCNVVtXopoKmpyZMnT3jy5Inah/HdunXjt99+49KlSzx8+JDx48erDfeUpaUlOjo6HD9+nIiICBISSn57HxQUxNy5c/Hw8GD37t1s3LiROXPmqA3bo0cP2rVrx5AhQzhx4gT+/v5cuXKFxYsXc+tWwZyjOTk53L17V+Xz+HHxVdCfx+jRozE3N2fw4MFcunQJPz8/Lly4wJw5cwgODsbPz48FCxZw9epVAgICOHnyJJ6ens9cV6AsuW89TsNZg7Dr0xKjuna0+2Yq2WmZ+P91RRmm3YapNF3wVqF9TmDTuTENZgzA0MWGBjMGYN2xIe4/HS8I8+Mxao3qgvM7nTB0qUHz5aPRtTXD69czyjAOA1pj2a4++g4W2PVuTrc98wk+fovw/AWK9R0tafThEExda6JnZ06Nbk3o+OMsYh/4E1XCTUtlCN1yGKtR3bEc2Q2d2rbUXDEBua054b+eBMBx4Shqb5ylso9eQyf0GjqhqaeNzMwIvYZO6NSxU34vkUmVYTRkUuQ2pug1dELbqfR1NqqyWz8fp+2MQdTu3RLzOnb0+2oq2emZPDlUUNb6/W8qneYVlDUNmSaWDRywbOCAppYUA2tTLBs4YOxopQzTc/UEGgzpwJHZ35OZko6ehRF6FkZIK/Fhyct6svU4jWYNwr5IffQrVB/bP0d9tOnYkCeF6mOrtROoOawDl2d8T1ZyOtoWRmhbGKGpXf3y6Hmkpqbh7umDu2deL7GQ0AjcPX0IC4+s5JiVr+ifD2LyVi+MR/REXssO68WTkdWwIHbnUQCsPhmP7ZeqI82069dEu35NNPS0kZoaoV2/JnIXe+X3Ok3qYNi7HTJ7K3RbNcRp+8q8qTy2/FmhaXsVD34+TrOZg3Dq0xKTunZ0/V/etcf7YEG96vr1VFp/+lahfU5g16kxTd8fgHEtG5q+PwDbNxryYGtBvbq/9TiWzWrRbOYgDJ2scBnSjvqjuvJox2mV88v0dXDu3xr33efLPa1l6ebPx2k/YxB18q/ZA76aSlZ6Jo8LXbMH/G8qnUu5ZuvnX7NNCl2zO3/yFnat6mJkZ45FXTs6fTICh7b1eVTo/0dVVF73S1JdOSYNHTBp6ACAvr0FJg0d0LXNm+5SU0dOk/lvYda8Fnq2Zpg0dqLNl5PRtTEh8HDVemgg6tqL8dlyFKdRXXEY2Rn92jVotGIMurbm+OXfKzdY+DbNN76vDO//6xl07cxptHwM+rVr4DCyM44ju+C9+YgyTO1Zg7Do1AhdB0v0XWpQa2o/7Ed0JGh/8WmWqqLyyBOXGQOo/+kI7ny4hdSgKOQWRsgtjNDUlSvDhJ+6Td05g7Hq0RRde3Ns+rbEZVo/Qo/drLjEP6fAH/6hxuhu2Izsgm5tW2qvHIfczpyQHacAqLVoJA02zlCGD/n1FNr25tReMRbd2rbYjOxCjVHdCPi+YJRR9Ek37Cb0xGpIe7QdLDDt1BjnT98m+uQt5SKOtRa+g3GbemjbW6BX3x7nBW9j0r4hEWqm8KqKxG9agcqqZz4/HsO0hQt1Zg9Gz8kKu6HtcRrbDb9tpyou8a8g68IhpG16Im3dA4mlHVqDJyExsSDryjEAtPqPQz7yg7zACgW54YEqH0VyAorsTHLDAyEzb41IDYc6aDZuh8TUCo2aDdCeshwkEjLPHlAfiSou89h+tLr0Q9apDxo1HNAe/T4aZpZknsm73sjfmoTO1E/zAisU5Ab7q3wUifGQlUlusD9kpOeF05Si4VALDYdaIJWiYWKe97dljUpJo1ACsdBwmaqar5VLYWhoWOJ3CxYswNfXlwEDBmBkZMSqVatKHSkglUr59ttvWblyJUuXLqVjx44lLlw8btw40tLSaN26NZqamsyaNavEBYMlEglHjx5l0aJFTJw4kaioKKytrenUqRNWVgU/7MnJyTRr1kxlX0dHR/z9/UvOgBLo6upy8eJFPv30U4YNG0ZSUhK2trZ0794dQ0ND0tLScHd3Z8eOHcTExGBjY8PMmTOZOvX5pgMqC4+/O4Kmthat101Ay0iX6Ds+nB35Odkp6cowerbmKr1Hom95cfn9TTT5dASunwwnOSCCy9M2EVNomGbA39fRMjGg8YdD0bE0Jt4jmPNjviAlJEYZRsfKmObLR+dNKxQZj+8fl3n4zV/K73OzsrF+oyH1JvVGqqdNamgsIWfu8uB/B4r1ZqlM0YeuIDUxwH7ucLQsTUh1D+Tx6LVk5Pd6kFmZIC+yiFXTM18q/63fpBYWb3YkPSgSt1Z5i1ZrWZuohLGdPhjb6YNJuPKIh8OWVUCqyt6NH44g09ai5+oJaBvqEnbXh31jPiezUFkzrKFa1vStTJhwbK3y79ZT+9N6an8Crz5hzztrAGg2Nm/OxZH7Fquc7+hHW3i4v+oM+X4ej787grRIfTzzAvWxSX59vFSkPtadkJdHvQ6o5tGVD7bgW4WGxZeVh+5eTJz1qfLv9Rt/BGBw3x6sWfxRZUWr3CX+c4lwEwMsZ72D1MKUDM8AAiYuJys0b2SI1MIErRoWKvu4/LNR+W+dxrUxHtyFzOAIPDtNAkAi18Jy7li0HKzJTUkj6bwbwXO/IjcppcLS9arubs6rVx1XT0BupEvkXR+OjP6crEL1ysDWXGW+0wg3L07P2ESrT0bQ6uPhJAZEcHr6JiILTZcSdc+XE+99Q5v5b9NizhCSgqK4svx3vIo8CHAZ1BYkErwPVa0FK5/l2g95+dY7/5odeteHPc+4ZhtYmTCp0DW77dT+tJ3an4CrT9iVf83WszBk4NfT0Lc0JiMplUj3IPaOW4//5aq9EGp53S+ZNnGm55+LlH+3WDEGAJ+9F7n24Y8ocnMxdLGh04g5yE0NyIhLJuaeLyeHriahis3jLeraiwk5dA0tE33qzR2G3NKYJPdgro5eT1r+/aO2lbHy5RBAamAUV0evp/GKsdR8tyfpEXHcX7yD0H8KHlxLdeU0+WwiOjam5KRnkuQditvM7wk5dK3Y+aui8sgT5wk90ZTLaP3zhyrncv/yT9y/zHvBfX/hDup/OoImn72L3MyI9Ig4/H89g/v/qt6DuchDV5GZGFBz7pvIrUxIdg/i3qjPSM/PIy1LY7QL5VF6YBR3R31G7ZXjsXu3NxkRcXgu2kbUPzeUYfy/PgAKcJ7/NnJrU7JiEok+6YbPuj3KMFoWRjTYNAO5lQnZSakkPw7k7jtrib34oOIS/wrEb1qByqpn8Xd9uT7xaxosfJu6c4eSGhjFgyW/EXzg3wpI9avLvnsZdA3Q6vU2EkNTcsMCSPtpJYq4vPtsiYEJGiYWzzhKETIZ8r6jkZhZQ0Y62U9ukbbra0ivPvfZhWVdP49E3xDtIWORGJuSG+xPypcLUMTkdczSMDZDw8zyhY4pMTHDYM2Pyr/l/d9G3v9tsp/cJWXt69uuE/7bJIpiK7kIRXXp0oWmTZvyzTffVHZUKt3OGmMqOwpVnlNu+rMD/cf9K9eu7ChUeTWq/pqFle7t+ysrOwpVnkfr2ZUdhSrtcpZxZUehyourVmNKK4eDuF4/U9LzzT75n2adJebIFV6dgeLZ0+L+1914TUevlqX6GeJ69Cw9RiU/O9B/XE5U9XzhUJGMfjvz7ECCipSl71TaufVW7nl2oGqm2o0UEARBEARBEARBEARBEARBEP5DFOKlZVkS/b8EQRAEQRAEQRAEQRAEQRAE4T9CjBR4DiWtMyAIgiAIgiAIgiAIgiAIgiCUsyq05ufrQIwUEARBEARBEARBEARBEARBEIT/CDFSQBAEQRAEQRAEQRAEQRAEQaiyFLliTYGyJEYKCIIgCIIgCIIgCIIgCIIgCMJ/hHgpIAiCIAiCIAiCIAiCIAiCIAj/EWL6IEEQBEEQBEEQBEEQBEEQBKHqEgsNlykxUkAQBEEQBEEQBEEQBEEQBEEQ/iPESAFBEARBEARBEARBEARBEASh6hIjBcqUGCkgCIIgCIIgCIIgCIIgCIIgCP8R4qWAIAiCIAiCIAiCIAiCIAiCIPxHiOmDhBciVYihOs/ipald2VGo8hwzRTl6Fv3c3MqOQpXn0Xp2ZUehyqt749vKjkKV5rxzfWVHocqT6OlWdhSqPk1xO/0sisTEyo5ClSextavsKFR94t7omW7OflDZUajyemSnVHYUqjx/ifjtf5ZJf4r+tc9SW2JV2VGo8lZXdgSqI4W4FyhL4komCIIgCIIgCIIgCIIgCIIgCP8RomuTIAiCIAiCIAiCIAiCIAiCUHWJhYbLlBgpIAiCIAiCIAiCIAiCIAiCIAj/EWKkgCAIgiAIgiAIgiAIgiAIglBlKcRIgTIlRgoIgiAIgiAIgiAIgiAIgiAIwn+EeCkgCIIgCIIgCIIgCIIgCIIgCP8RYvogQRAEQRAEQRAEQRAEQRAEoeoS0weVKTFSQBAEQRAEQRAEQRAEQRAEQRD+I8RIAUEQBEEQBEEQBEEQBEEQBKHqys2t7Bi8VsRIAUEQBEEQBEEQBEEQBEEQBEH4jxAvBQRBEARBEARBEARBEARBEAThP0K8FKimli9fTtOmTSs7GoIgCIIgCIIgCIIgCIIgCOUrV1F5n9dQhawpMGHCBOLj4zl48OBzhZdIJPz1118MGTKkXOP1vM6fP0/Xrl2Ji4vD2NhYuT0yMpIlS5Zw7NgxIiIiMDExoUmTJixfvpx27dqVa5w+/vhjZs2aVa7nKAsNPxpGrTHdkBnpEXvHG7cF20n0DCl1H7v+rWg0bwT6jpYkB0Ty4LN9hBy7pRLGZXwP6k7vj46lMQmeIdxZ+hvR1z1e6NzaFkY0WToKq06NkOlrk+QTxuMNfxP8zw2V49h0b0rDuUMxqu9ATloGUdfc+XfSN6+WMS+o6dxh1BndFS0jPaLv+HBt0Xbin5GPjv1a0eyT4Rg4WpIUEMntz/8g8HhBPtYd1526Y7ujb28BQLxnMPe+/ouQc/cBkEg1aT5vOHbdmqLvaEFWYhqhlx/itnYvaRHx5ZZWdap7OTJp7ITroncwbeqMIieX4KM3ubvsd7JTM8ogd57NcUJPnGcMQG5pTLJHMI+W/EpckXQWZtquPg1WjEG/rh0ZEXH4bDpC4K+n1Ya1GdKO5ltmE37sJm4T/qfcXvvjN6nzyXCVsOmR8Zxp/H7ZJKqcmY7ph/l7w5BampLhGUjY6p9IvflIbViphQnWiyah08gFLacaxOw4TPiqn4oE0sTi/RGYDOuO1NqMDN8QIj7fRvLF2xWQmsp16+4Dtu3az2N3b6JiYtmwbgndO7Wv7GhVCKlrZ6TNeyLRM0IRE0rmxT/IDfUueQdNKbLW/dGs1xqJriGK5Hiybh4j5/EVACSmNsjaDUTD0hENQzMyL+wj++7ZCkpN+dh7x58dN32JTs6glrk+n3RrSHM70xLDZ2bnsOWqF0cfhxKdkoGVvjaT27kwpLE9AIceBrHs2P1i+13/sA9yqWa5paM87b3tx47r3kQnp1PL3IBPejSmub1ZieEzs3PY8q8HRx8F5+WRgTaT29VhSBNHZZjE9Cw2XXzMWY8wEtOzsDXWZW63RnSsZVURSSpzoq49295rHuy4/IjopDRqWRrzSf+WNHdS//97yf5/OXzHt9h2Z0sjDswZBIB3RDybz9zjcUgMYfEpfNyvJWM61C/XNJS3vdc92XH5MdHJ+XnUtwXNnSzVhl1y4Kr6PLIw4sDsAUB+Hp29z+PQ2Lw86tuCMe3rlWsaKprNhF7YTx+MlqUxKR7B+CzdRuJ1d7VhtSyNcV4+Hn1XZ3ScrQnZegzfpdsrNsIVwGJ8H2ymDUFmaUKaZxCBy34m+cYTtWFllibYL52ArmsttGvaEPHLPwQt+6XEY5sOeoNamz8i7vh1vCd9Vl5JeCW18ttX2pbGJHqGcFdN+6ow83b1aLp8DIZ1bEmLiMfj+yP4/npGJYxtfrtOz9GSlPx2XWihdp1EU4MGH7+J47D2aFsYkxYZj//eizz55iAo8h7k2fZrifPY7pi41kRuasDJHgtJeBRQLnlQVoZ/8A7dR/VC30gPrzte/LJkC8FeQSWGb92nLUNmDMfa0QZNmSbhfmEc+ekQl/46rwwzZPqbtO7Tlhq17MhMz8DTzYOdn+0gzDe0AlJU9rp98CYtR3ZDx0iP4LveHF6yjUivkp8TWNa2pfvcEdRoXBMTOwv+WfkrV385rhJGQ1ODbh+8SZMhHdC3MCYpMp47+y9wfuNBFIrX88Gw8N/1Wo8UyMrKKtfjv/nmm9y7d48dO3bg6enJ33//TZcuXYiNjX3pYz5vnPX19TEzK7lBWBXUmzGAulP74bZoO6f7LiE9MoEuexcg1dMucR+zFi60+2EWAfsvc6LHAgL2X6b9llmYNqulDGM/qC1NV47lyYZDnOi1iOjr7nTaOQ9d24L8eJ5zt9n4Pga1bLg8/iuOd51P8NFbtNsyC+NGBY1mu/6taLPxffz2XuREjwWcGbSCgANXyjinStdo+gAaTOnLtcU7ONJ/KWlR8fTaPb/UfLRo4ULnzTPx+fMyf/dciM+fl+nyw0zMC+VjSlgsbuv2cqTfEo70W0LYv4/p9stcjOvYAiDV0cKssRP3NhzkcJ8lnHvvG4ycbei+bW65p7mw6l6OtK2M6bx3Acn+EZzuv4yLo9ZjVMeO1humlUNuFWczuC0NVo3D+5uDXO6xgNjrHrTePR9tW/XXDx0HC1rtmkfsdQ8u91iA94ZDNFwzHuv+rYuHtTOn/rLRxFxV39BJcg/idKNpys+lLvPKNG3lxbB/R6wXv0fUd/vwGTCblFuPcPxlObIaFmrDS7RkZMckEvXdPtKf+KkNY/XRWExH9iV0xRa8er1P3K6jOPywCO0GzuWZlCohLS2dui7OLJw7vbKjUqE0a7dA1mkEWTePkb5rDTmh3sgHz0RiYFLiPlp930PDvi6Zp38j/dflZB7/GUVcuPJ7iUwLRUI0Wf/+hSIloQJSUb5OuIfyxdnHTG7rwp7xb9DMzpQZ+28QlphW4j7zDt/hRkAMy3q7cnBSZ9YNbIaTqZ5KGH0tKaff767yqa4vBE48CeGL0w+Y3L4Oe97tQjN7M2bsu0pYQmqJ+8w7eIsbAdEs69eUg+91Z92gljiZGSi/z8rJZdqeK4QmpPHF0FYcnNKdpX2aYqlf8u9qVSbq2rOduO/PF0dvMblzY/bMGEAzJ0tm7DhLWHyK2vDzBrTi9Pzhys+JecMw0tGiZ6F75PSsbGxN9JnTuxnm+joVlZRyc+KBP18cc2Ny50bseb8fzRwtmPHbuZLzqF8LTs8bpvyc+Hhofh45KMOkZ+Xk5VHPpphX0/pVGovB7am18l0Cv/kTt57zSLj+hMa7FiG3NVcbXiKXkRWTSOCGP0mp4g9jX5bpoA44LJ9I6Lf7edT7I5JuPKbO70vQqlFCnmhJyY5JJOzb/aQ+9i/12Fq2FtgvHU/SNfWdVKoCu0Ltq1O9FhF13Z2OO+ehU0K7Q9fego6/f0LUdXdO9VqE+7eHaLZqHLb9WynDmLZwoW1+u+5UfruuXZF2Xd2ZA6k1rju3F+7geKdPuL9qN3Wn98dlUi9lGE1dbaJvePJgzZ7yy4AyNGjaUPpPHsS2pT+ycOAnJETFsWjnCrRLaQMnxyfz16Y/WDLsU+b1/oDzf5zh/S9n0aRTU2WY+m0acuLXYyweMo81Y5ajIdVg0W/LkevIKyBVZavjtIG0n9SXI0u3s3nQYpKiEpjw+0K0SskjmY6c2MBITn6+h6TIuBKP22p0Dw4v3c6GHh9zYt0u3pgygLYTepdXUoQXIUYKlKkKfynQpUsXZs+ezbx58zA1NcXa2prly5crv3dycgJg6NChSCQS5d8Ahw8fpkWLFmhra+Ps7MyKFSvIzs5Wfi+RSPjhhx8YPHgwenp6rF69WjnNzm+//YaTkxNGRka88847JCUlKfdTKBSsX78eZ2dndHR0aNKkCfv37wfA39+frl27AmBiYoJEIlGOfLh8+TKff/45Xbt2xdHRkdatW7NgwQL69++vPHZCQgJTpkzB0tISQ0NDunXrxr1795TfP43fL7/8grOzM3K5nC1btmBra0tukVW1Bw0axPjx41X2K+yXX36hYcOGyOVybGxsmDlz5nPHozzUea8PjzccJOToLRI8grk+5wc0dbRwHFZyD9E67/Ul4uJDnmz8myTvMJ5s/JuIy4+o814fZZi6U/vit/s8vrvOk+QVyp2lv5MWGkOt8T1e6NxmLWvj9ctJYu/6khIYxeNvDpKVkIJJYycgr8dBs5XjuLdqFz6/niHZN5wkn7BiIwnKW4PJfbj/7SECj90i3iOYSx9sQaqjhfPQkvOxweQ+hF58yINNh0nwCePBpsOEXX5Mg8kF+Rh86g4hZ++R6BtOom84dz7/g+yUdCyauwCQlZTGyZGf43/4Ook+YUTd9uHa4l8xb+KMXo2KeyFV3ctRjZ7NUGTn4LZgO0k+YcTe88Vt4XbsB7RGv4QeemWp5rT+BO06R9DOcyR7hfJ4ya+kh8TgOKGn2vCO43qQHhzD4yW/kuwVStDOcwTtPo/z9P6qATUkNP1+Bl5f7Cc1IFLtsXKzc8iISlB+MmOS1IaraswnDSHuj1PE7TtJhk8w4at+IissGtPR/dSGzwqJJHzVj8T/dZbcJPUP6oyHdCVq8z6Sz98iKyiC2J3HSL54G/PJQ8szKVVCx3atmD1lPD27dKjsqFQoafMeZD/6l5xH/6KICyfr4h8okuOQNu6sNryGYwM07WqTcWgTuUHuKJJiyI3wJzesoCdqbkQAWZcPkON5C0VOttrjVCe/3fJjaGN7hrk64GxmwLxuDbE20OaPu+ofFv3rF8mtoBg2vdmKtk7m2Brp0tjGmKa2RUYWSMBcX1vlU139dsOboU0cGdbEEWdzA+b1aIy1oQ5/3PFXG/5f3whuBUWzaURb2jpZYmusS+MaJjQtNPri4P0AEtMz+XpYa5rZmVHDSJdm9mbUtTKqoFSVLVHXnu23fx8ztIULw1rVxtnSiHn9W2FtpMsfJfTeNdDWwtxAR/l5FBJDYnomg5sXPIRrZGfO3L4t6ONaE5m0+vcz++2KO0Ob12JYS5e8POrXEmtDXf644ak2fMl5VPCyv5GdGXP7NKePqxOyavpisjS2UwcQvvss4bvOkuYVgu/S7WSERGMzvpfa8BlBUfgs2UbkHxfJLuF+qbqzem8Q0XvOEL37NOnewQQt+4XM0Bgsx/VRGz4zOIrAZT8Ts/88OYml5ImGBs6bPiTkyz1kBEaUU+xfXZ389pVffvvq3tLfSS3Sviqs1rjupIbEcG/p7yR5heK36zx+ey5Qd1pBu+Npu849v13nvvFvIi8/onahdp1Zi9qEHncj/MxdUoOjCfnnBhEXHmDapKA+Bu6/zJOv/yLi4sPyy4Ay1G/SQP7a9Ac3jl8jyDOQ7z7agFxbzhuDO5W4z+NrD7l54joh3sFEBIZzbNsRAt39qduqgTLMuvErubD/LMFeQQQ88WfzxxuxsLPEuXGtEo9bVbWf2IcL3x3i8YmbRHoG8+dHm5HpaNFkcMnPCULu+3Ji3S4eHL5Kdqb633f75rVxP3ULz3N3iQ+O5tGxG3hfeoBt45rllRRBqDSVcge3Y8cO9PT0uH79OuvXr2flypWcOnUKgJs3bwKwbds2wsLClH+fOHGCMWPGMHv2bB4/fsyWLVvYvn07a9asUTn2smXLGDx4MA8ePGDixIkA+Pj4cPDgQY4cOcKRI0e4cOECn31WMNxu8eLFbNu2jc2bN/Po0SM+/PBDxowZw4ULF7C3t+fPP/8EwMPDg7CwMDZs2IC+vj76+vocPHiQjAz1U4AoFAr69+9PeHg4R48exc3NjebNm9O9e3eV0QTe3t7s27ePP//8k7t37zJ8+HCio6M5d+6cMkxcXBwnTpxg9OjRas+1efNmZsyYwZQpU3jw4AF///03Li4uLxSPsqTnYIGOlQnhFx4ot+VmZhN11R2zlrVL3M+spQvhF1SH/Yefv495qzoAaMg0MXGtqXJcgPALDzDPP+7znjv6hgcOg9qiZawHEgn2g9uiIZcReSWv17NJYyd0a5hCroJeJ9cw6O4mOu2ch2F+T/qKoO9gga6VMaFF0hJ+zR3LUvLRooULoRdV8yjkwv0S95FoSKg5qC1SXTmRbl4lHlfLUAdFbi6Zpd20lqHXoRxpasnIzcxWDl0FyEnPBMC8dd0Xyo8XJZFpYuRak6jzqnkRdeE+Ji3rqN3HuGVtoorkXdS5exg1cUZSqEFb+6M3yYxJImjX+RLPr+dsTfd739P15gaabZmFjqP6IfhViUQmRaeRC8mX7qhsT750B93mLz/kX6IlQ5GRqbItNyMT3ZYNSthDqNY0NNGwdCA3UHUUTU7AEzRs1I8O0XRuQm5EALKWvdCe9Bna41Yge+NN0JRVRIwrXFZOLk/CE2jnpDoCp62TBfdC1PfcOu8dQUMrI7bf8KXn5tMM2nqe/517THpWjkq4tMwc+m45S6/NZ5j1503cI6pnT++S88iSeyHq79/Oe4XT0NqY7de96LnpBIO2nOZ/Zx+q5NF5r3BcbU1Zd/I+3b49zptbz7L1iic51bEHlKhrz5SVncOT0FjaudiobG/rUoN7gVHPdYyDt7xpU8uGGib65RHFSldyHtlwLyj6uY5x8LYPbZytqWH8euZRURKZFANXZ+LOq3Yyi7twH8NW5Xt/W1VJZFL0XGuRcOGuyvbEC3fRa/lq00bV+PAtsmMSiN5z5tmBK4mkhPZVRKH2VVFmLWsTUbQ9dv4+Jk1qKtsdZi1diFDTrjNrVdCWib7hgWXHhug7WwNg1MAB89Z1CTtz91WTVSks7a0wsTTl/qW7ym3Zmdk8vv6QOi2evyw16uCKjbMtT66XPLpE10AXyBtlUJ2Y2FtiYGmC96WCspGTmY3/9Sc4tFDfzn1egbc8cO7QCLOaeeXJur4Dji3r4nn+7isdVygbCoWi0j6vowpZU6AoV1dXli1bBkDt2rXZtGkTZ86coWfPnlhY5DV8jI2Nsba2Vu6zZs0a5s+fr+wp7+zszKpVq5g3b57yWACjRo1Svgx4Kjc3l+3bt2NgkDd0euzYsZw5c4Y1a9aQkpLC//73P86ePatcB8DZ2ZnLly+zZcsWOnfujKlpXu8qS0tLlTUFtm/fznvvvccPP/xA8+bN6dy5M++88w6urq4AnDt3jgcPHhAZGYlcnjcc68svv+TgwYPs37+fKVOmAJCZmclvv/2mTDtAnz592LVrF927dwfgjz/+wNTUVPl3UatXr+ajjz5izpw5ym2tWrV6oXgUlZGRUeyFR5YiB5nk2T1dtC3z8ik9SrUhnh6dgK6d+uGTANoWxqRHJaruE5WItkVe7zUtUwM0pJrFjxuVoAzzvOe+OnUj7bbMYuiTH8nNyiY7LZN/J35NSn6vZ738B5gNP36Tu8t/JyUoirpT+9PtryUc7fARmSUMJy5LOvlpSYtWTUtaVAL6peSjjoUxaVHF99GxUO0FaFzPjv5/L0dTLiM7JZ2zk78hwUv9fIKachktFryN719XyUoueWqHsvQ6lKOIy49ounw0dd/vj9fW42jqymm84C0AdKyMnyMXXp6WqSEaUk0yi6QhIyoBuaX6HqFyS2MyioTPjEpAQyZFy9SAjMh4TFrVwX5UFy51X1DiueNve3Nv5mZSfMPQsjCi9gdDaX9kBRc7fUJWXNW96dQ0MUQi1SQ7WvWhZE5MHFKL5i993ORLtzGbOISUG4/IDAhDr0MTDHu0AY3Xr+egABIdfSQamihSVa9DirREJHqGavfRMDRHo4YL5GSReeQH0NFHq+tIJNq6ZJ7+rSKiXaHi0jLJUSgw1dNS2W6mJyc6RX1ni5D4NO6ExKEl1eR/Q1oSn5bJ2lMPSUjPYkXfJgDUNNVnZV9XXCwMScnMZpebHxN2XWHvhE44muipPW5VFZeakZ9HqiMd8vIoXe0+IfGp3AmOzcujYa3z8ujEPRLSsljRv5kyzM2AaPo1tGPTW20JjE1m3cn75OQqmPpG9XqYJ+ras8WlZpCTq8C0yIgZM31topPVl6PCohJT+dcrlLVvvVFeUax0peZR0rPveaOS0vLyaPh/Z0SczNQAiVSTrKh4le2ZUfGYWBhXSpwqmzQ/T7Kj41W2Z0XHY5jfrngZ+i3rYTGyO496VuwUri9Knt++KtqOKNy+KkrbwqhYWysjv90hNzUgPTL+me06AI9Nh5EZ6tLn0hcocnKRaGrw8LM/CDp4tYxSV7GM88tLQpH6lRCdgIWt+ulMn9Ix0OWH6z8j1ZKRm5PLz0u28OByyTNEjFsykSc3HhPkGfiq0a5Q+vn//5OLlJ/kqESMS3lO8Dwubj6M3ECXOWe+VJan01/u4/7f1bM8CUJpKu2lQGE2NjZERqqffuIpNzc3bt68qTIyICcnh/T0dFJTU9HVzXvD2bJly2L7Ojk5KV8IFD3f48ePSU9Pp2dP1ak0MjMzadasWalxevPNN+nfvz+XLl3i6tWrHD9+nPXr17N161YmTJiAm5sbycnJxeb+T0tLw8fHR/m3o6OjygsBgNGjRzNlyhS+//575HI5O3fu5J133kFTs/gDpMjISEJDQ0t8YfC88Shq3bp1rFixQjXNeo0YYeBaLKzjsPa0WD9J+felsV/k/aPoyzSJRKXHtFpFvpdIim8r9rdEouY4pZ+78acj0DLS49yItWTGJmHbpyXtf5zN2SGrSHAPQqKRN5Dm8YaDBP+TN2LlxodbGHh7I/YD2+DzW9kvOOc8tD3tPi94qXV63Jdq0yKRSIqnryi1+6huTPQJ4+9ei9Ay1MWxXys6fjOVY2+uLvZiQCLVpPP3M5BoaHBt4fYXSNGLeR3LUaJnCNfnbKHp8tG4LnwbRU4uXj+fIC0yHkVOLhWheBKekX/q0gygUKCpp03T72fw4KOfyIoteTqgqLOFbj6fBBF/y4su17/B7q1O+G05+kLxrxTFsuc5ylwpwlb+iO3aWdQ+tRkUkBkYRtz+05gMVz+cWnhNFCszpVy78y5SZBz/BTLzHtRlXfwDrf5T4NweyCnfdZIqiwTV665CoVB7KQbIzf9u7YCmGMjzenV/3LU+Hx+6zYIejdCWaeJawwTXGgVzyTe1NeGdHZfZc9ufT7s3LLd0lKei+aFAUSzfnlLm0cAWGGjn51H3Rnz8100W9HJFW6ZJrkKBqZ6cJX2aoqkhoYG1MVHJ6ey47l3tXgooibr2TBJJ0bpGCaVI1d93fDDQ1qJbffvyiVgVUjQ/FIri+abO37ef5pFd+USsCite9V7tful1UJZ5oqGnjfPGD/D/ZDPZcdVjGs5ivWklktKbrSW0O1SO84x2nf3gtjgO68D16d+R4BGCcSNHmq4YQ1p4HAF/XHrhNFS0N4Z04r217yv//uzd1UBJzdDSy1J6chrz+n6Itp4OjTu4Mm7xRCIDI3h8rfi0SRNXTcGhnhPLhpfc0auqaDK4A4PWFjwn+G3iekB9c/5Ve3Q3HtiOpkPe4I853xHpGYxNA0f6LR1LUkQcd/6s+uVJEF5EpbwUkMlUh+dKJJJi8+cXlZuby4oVKxg2bFix77S1C3p16OkV7wVW2vme/veff/7B1lZ1WpinvepLo62tTc+ePenZsydLly5l8uTJLFu2jAkTJpCbm4uNjQ3nz58vtl/hEQfq4jxw4EByc3P5559/aNWqFZcuXeJ///uf2jjo6JS+uNfzxqOoBQsWMHeuao+Ev+uoH1UQcuI2MbcLXjBoaOUVLW1LI9Ij45Xbtc0Mi/UGKCw9Kh7tIj2Y5eaGpEfn9Q7IjE0iNztH2YtbeVzzguM+PV9p59ZztKT2pN4c6zyPRM+81enjHwdi3qYuLu/2xO3TX0iPyNv36feQN31MSkCkymK0ZSnw5G2i7hTko2Z+PupYGJFWOC3mhsVGDxSWFhWPTpF8zNtHtZdFblYOSf5581LG3PfDvKkzDSb34eqnvyjDSKSadPlhFvoOFpx4a125jhJ4HcsRQOBfVwj86wpyc0NyUjNQKKDO1H4kB5b+MvRVZcYmkpudg7xI7xwtc0MyivS4eSojMr7YKAItc0Nys7LJjEvGoK4dug6WtPztE+X3Eo28m/e+x9KH3gABAABJREFUIb9zof1ctWsM5KRmkPQkCD1n62LfVSU5cYkosnOQWqguUKlpZlys59cLHTc2kcBpa5BoydA0MSQ7IgarTyeQGVR154UVXp4iLRlFbg4SPdW6JNExKNajWblPSgKK5HjlQ0qA3NhwJBINJAYmKOLL93pR0Ux0tNCUSIgpMiogNjUTM13191/m+nIs9bWVLwQAaprpowAiktPVjgTQkEhoaGNEYFz5j+4raya68rw8KtKbOzYlEzO9kvJIG0t9HeULAYCaZgZ5eZSUhqOpPhb62kg1JGhqSFTCRKdkkJWTi0yz+swPL+ras5noytHUkBBTpMd7bEo6Zs9Yb0OhUHDQzYf+TWu+lnPiP6XMo2J17Tnz6LYv/Zu83nlUVFZsEorsHLSK3EtrmRuRWUob5XWWnZ8nsiIjJWRmRmSV0m4pjdzJGrmDFbW3LyzYmH/tbhmwnwedZpIREF7C3hUro5T2VdHRA0+lRyWobbM9bXfkhSm9XQfgumQU7psOE3ToGgCJ7kHo2ZlTb/agavFS4NapG3jdKVi/RKaV9xtubGFMfKHFcA3NjEh4RntEoVAQkV8mAh77Yetix5DpbxZ7KfDuivdo0aM1y99aSGx4TBmlpPw8Oe1G0F1v5d/S/OcEBpZGJBcaUaFnbkjKK16D+iwYxcXNf/PgcN7IgAiPIIxtzek0fbB4KVAVVMfpLquwKnnXL5PJyMlRnR+2efPmeHh44OLiUuyjofHyyWjQoAFyuZzAwMBix7W3z+sRo6WVN7S9aJxKOl5KSooyzuHh4Uil0mLHNjcvfUiTjo4Ow4YNY+fOnezevZs6derQokULtWENDAxwcnLizBn1cwy+bDzkcjmGhoYqn5KmDspOSSfZP0L5SfQMIS0iDutOjZVhNGSaWLSrR8ytkuesj7nljVWhfQCsO7sSfTPvRzI3K4e4+35Yd2qkEsaqU2Oi84+bEhj1zHNLdfIa00XfIityc5UPOGPv+5GTnolBrYL5RSVSTfTsLUgJfr75RV9Udko6Sf4Ryk+8ZwipEfHUKJReDZkm1m3rEVlKPka5eVOjo2oe1ejUuNR9AJBIlC8ioOCFgGFNK068/RkZ5Tzty+tYjgrLiE4kOzUDh8Ftyc3ILPeFrhRZOSTc98Ois+roHvNOjYm7pX7hvPhbXpgXyTuLLq4k3PNFkZ1DsncoFzp/wqXu85WfiBNuxPz7mEvd55MWqv6mUkNLin7tGsqXbVWVIiubtIfe6L/RVGW7/htNSb3t/urHz8wiOyIGpJoY9m5P0unrr3xMoQrKzSE3MhANh/oqmzUd6qssZlpYTpgPEj1jkBU87NUwsUKRm4siSf0c+9WZTFOD+tZGXA1QndP8ekA0TWxN1O7T1NaUqOR0UgstDBcQm4KGBKxKeHCnUCjwiEzEvISH6FWZMo/8i+SRfyRNii6unK+pnbo8Ss7LI4O8TiRN7EwJjEsht9BvV0BsMhb68mr1QgAQde05yKSa1K9hylXvMJXt173DaOJQ+jQUt/wiCIpJYmhLl/KMYqVT5pFPkTzyCaOJfelttlv+kQTFJjG0RfVbpPNVKLKySbrvi0mRe0zjzq4k3lS/gPXrTpGVTcp9H4w6NVHZbtipCSm3Xu4eMt07hIfd5vCo11zlJ/7kTZKuPORRr7lkhpZPm/RlKPLbV1altK+KirnlpabN1pi4e34osnPywxRv11l1diXmZkFbRlNHC0WRTqaKnNznGulTFaSnpBMREK78BHsFERcZi2uh9oimTEqDNo3wdHvBsiSRINVS7ST77sr3aN2nLatGLiEqqHq8CM9MSSc2IEL5ifQKISkyjlpvFJQNTZkmTm3qE+imvp37vGQ6WsXa97m51ac8CcKLqJSRAs/y9AF3hw4dkMvlmJiYsHTpUgYMGIC9vT0jRoxAQ0OD+/fv8+DBA1avXv3S5zIwMODjjz/mww8/JDc3lzfeeIPExESuXLmCvr4+48ePx9HREYlEwpEjR+jXrx86OjpkZGQwYsQIJk6ciKurKwYGBty6dYv169czePBgAHr06EG7du0YMmQIn3/+OXXr1iU0NJSjR48yZMgQtVMdFTZ69GgGDhzIo0ePGDNmTKlhly9fzrRp07C0tKRv374kJSXx77//MmvWrFeOx8vy/Ok49WcPIskvnGTfcOrPHkxOWiYBB64ow7T5dhqp4XE8WLs3b5+tx+n21xLqzRhAyAk3bHu3wKpjQ84MXqncx2PLMdpsfJ/Ye35Eu3lRa0w3dG3N8Pn1zHOfO9E7lCTfcFqun8S9FTvJiEvGrk9LrDs14tLYvCl7spPT8PntDI0+Hk5qaCypwdHUe78/AEGHK+5B3uOtx3GdNYhEvwgS/cJxnTWI7LRMfP8qyMc3NkwlNSyO25/ty9vn5xP0/XMxjaYPIOiEG/a9W1CjY0OODl2l3Kf5/LcIPnuP1NAYpPra1BzcDut29Tk1Om8onkRTg64/zsassROnx3+FhqaGck2CjPhkcrOe/ZKsLFT3cgTg8m5PYm55kZWSjnWnxjRZOpL7a/aSVQELNvv98A9NN80g/p4v8bc8sR/bHR07cwJ3nAag7qJ30LY24d6szQAE/Hoax0m9qL9iDEG/n8W4ZR3sR3XlzrSNAORmZJHsHqxyjqyEvHQU3l5/2WgiTt4mLSQaubkhLh8ORWqgQ8i+i+We5lcV/fNB7L6aS9oDb9JuP8FkZB9kNSyI3Zk37ZHVJ+ORWpkR8nHB6C3t+jWBvGHeUlMjtOvXRJGVTYZ3EAA6TeogszYj7bEvMmtzLOeMQqKhQdSWPys+gRUsNTWNwOCCKclCQiNw9/TByNAAG+uqv/j0y8q+fRqt3u+SGxFAbpgv0sYdkRiYkP0grw7I2g9Bom9M5sntAOR43ETRuh9aPceRde0IEm09ZG8MI+fxlYLpTDQ0kZjaFPxb3xiJuR1kZaBIeL4FQ6uSsS1rsuifuzS0Nsa1hjF/3gsiLDGN4U0cAPj2ojuRSems7t8UgH71a/DTVS+WHrvH+x3qEJ+WydcX3Bnc2B5tWV6nhR/+9cS1hgkOJnokZ2Sx+7Y/npGJLOjRqKRoVGljW7uw6LBbXh7ZmvLnXf+8PGrmBMC35x8TmZTG6oF5HUf6NbDjp389WPrPHd7vWJf41Ey+PveIwa6Oyjx6q1lN9rj5sv7UA0a2dCYgNpmfr3oxsmXNykrmKxF17dnGdmjAov3/0tDWDFcHC/686UlYQgrDW+ctxvjtidtEJqaxeoTqnPgH3bxpbG+Oi1XxF3VZ2Tn4ROb1xszOySUyMRX30Fh05VIczNSv51CVjW1fj0V/XqVhDTNc7c3585Y3YQmpDG+dt0Dqtyfv5OXR8PYq+x1086axnRkuataJysrOwSeqSB6FxaKrJcPBzKBY+OomZMsR6m6cRdI9HxJveWIzpgfatuaE/XoSAKeFo5DbmOIxa5NyH72GTgBo6mmjZWaIXkMnFFnZpHoGqztFtRPx09/U3DCHlHs+JLt5YDGmJ1q25kT+dgIAu/ljkNmY4jfnW+U+OoXyRGZqiE5DJxSZ2aR7BaPIyCLNQ3Wu95zEvA6IRbdXBZ757au4e37EuHnhnN++8s1vXzVa+DY61ibcnP0DAD6/nsFlYk+aLB+N785zmLWoTc2RXbg2vaDMeG09Tpe/llB3xgBCT7hRI79dd65Quy7s1B3qzxlCakgMiR7BGDd2os7UvvjtvqAMIzPWQ9fWXLmm29OOf+mR8SWOZKhMR38+zJAZwwnzDyXcL4whM4eTkZ7B5UMFbakZ/5tDbHgMu9f/DsCQ6W/ic9+biIBwpFpSmnVtQadhXfh58Q/KfSatnkqHQZ344r21pKWkYZQ/siU1MZWsjMwKTeOruvLLcTrPGEyMfzgxfuF0njGYrLRM7h0qeE7w5lfvkxgRy6n1ec8JNGWaWNS2y/+3FEMrU6wbOCpfOgC4n7lN5xmDiQ+JJtIrGJuGTnSY1A+3P85XeBoFNcRIgTJVJV8KfPXVV8ydO5effvoJW1tb/P396d27N0eOHGHlypWsX78emUxGvXr1mDx58iufb9WqVVhaWrJu3Tp8fX0xNjamefPmLFyYN0zP1taWFStWMH/+fN59913GjRvHli1baNOmDV9//TU+Pj5kZWVhb2/Pe++9p9xPIpFw9OhRFi1axMSJE4mKisLa2ppOnTphZWX1zHh169YNU1NTPDw8GDVqVKlhx48fT3p6Ol9//TUff/wx5ubmDB8+vEzi8bLcvzuCprYWLdZNQMtIj5g7Plx45zOyCy2Op2trhqJQpY655cXVaZtoPH8EjeaNICUggivTNhJbaEqdoL+vITfRp+HcoWhbGpPgEcylMV+QWqj3/rPOrcjO4eKY9bgueoeOv36MVE9Osl8E1+dsIazQXOh3V+4mNzuXthvfR1Nbi5jb3pwbvkb5ELQiPPz+CFJtLdqunYDcSJeoOz6cHPW5Sj7q1zBXuThG3fLiwvRNNJ83gmafDCcpIILz728iulA+apsb0unbaehYGpOZlErckyBOjV5P2KW83ut6NqY49M57yDD41FqVOB0fvobwq0/KM9lKr0M5MmtWi0Yfv4lUT5sk71BuzfuFgP2XyzPblMIOXUPLxIDac4chtzIm2T2Im6M+Jy0/nXJLY3RsC3rBpQVGcXPUehqsHIvju73IiIjj0aIdhP9z44XOq13DlGY/zELL1IDMmETi3Ly40m+p8rxVWeI/lwg3McBy1jtILUzJ8AwgYOJyskLzHgRJLUzQqqHau9Lln43Kf+s0ro3x4C5kBkfg2Slv7kuJXAvLuWPRcrAmNyWNpPNuBM/9ityk6jelyYt66O7FxFmfKv9ev/FHAAb37cGaxR9VVrTKXY6XG1k6+sja9Eeia4giJpSMQ5tQJMUCINEzQmJQqLd3VgYZf21Aq8s7aL+zAEV6ct4xrvytDCLRM0Zn9GLl3xotevF/9u4zOqqiD8D4s9lseu8hECCETgApUpQqHZQiCNIFFKVYUFBApApY8QWk2Ggigog0pUqT3muAhIT03nvb3fdDcJMlG0BJxf/vnD2HvTtzd2aYu7lzp6mad0Md5kf2r4aXGKzIuterQlJmDqtP+hOXno23kxXLX2xJFdv8faJi07KJLLTkiYWJMasGtWLxnzcYtuE4tuYmdKvrzsRC6+CnZucxf/814tKzsTI1pp6LDd8PaYOPu11ZZ69EdK/vkV9GJ27fKyNrlg9qXaiMsohMua+MhrRl8YFrDFt7DFtzFd3qeTCxfcFIejcbc1YObsvnf15n0PeHcbE2Y2gLL15pXbvM81cS5Fp7uO6Na5CUkc3qw1eJS83E29WO5SM7U8XeCsjfKDcyWf/vUWpWDn/eCGFq75YGzxmTmsmQr3/XvV9/3Jf1x31pXtOV78d1K73MlJLuPjVIyshh9ZFrBWU0oiNV7O6VUVqW4TLyDWVqL8MDrGJSMxmyYo/u/foTN1l/4ibNa7jw/diuBuNUJrE7TmJsb0X1KQMxcbEn/VYo14ctJPvevZ6Jqz2mHvozLZr/+Znu39ZNauHyYjuyQmM423Jimaa9tCTsPIHS3poq77yEysWezNsh+I1YQE54/j2kyrXoPWSj/Ut0/7Zs4o3jgA5kh8ZwtfX4Mk17SQi7175qcK99lXJf+8rcxU5vGd6M0Fj+Gv4ZTecOp9bormRFJ3Jp1nrC7+3nB/ntutOvL6fRvXZdWnA0p+9r112auY6G7w+k2eJXMHO0ITM6kYANh/D9cpsuTJVuzXn6fwVl2mb1ZABufP4rvl8UhKsodq76DRMzU8YuGI+ljRV3LvuxcPgcsgq1gR2rOKMp1AY2tcgP7+juSE5WDuEB4Sx/ewmndp/Qhek2oicAc7YU7NUJsOLdpRzdWvJ7Jpamv1btQmVmwgvzX8HM1pKwywGsHbGInEJlZOfhiFZbMIvE2tWeSX8s0r1vN74P7cb34e5pX74fkj/YePfsdXR5dxAvzH8FSydbUqMTOffTnxxeWvHqiRCPS6F93F04xH/KZvdh5Z2ECi/TwLIxQp+59O4+lNVD9lkRUN2ycmy4Vp7qnl368ED/YbkbPy3vJFR4CkuL8k5CxaeskGNsKhRtiuH1/UUBhcd/b5Paf0zujR7q3JvXyjsJFZ65Mu/hgf7jgjTyt/9htpo8+QN7HldthdSjh1kQ9FN5J6HSSSnHTnWb7w+U23eXFmnFCCGEEEIIIYQQQgghhKiwtDLAtERVst3EhBBCCCGEEEIIIYQQQgjxb8lMASGEEEIIIYQQQgghhBAVl8wUKFEyU0AIIYQQQgghhBBCCCGE+I+QmQJCCCGEEEIIIYQQQgghKi5NeSfgySIzBYQQQgghhBBCCCGEEEKI/wjpFBBCCCGEEEIIIYQQQggh/iOkU0AIIYQQQgghhBBCCCFEhaXVaMvt9W+sWLGCmjVrYmZmRvPmzfnrr78eKd6JEycwNjamadOm/+p7H5V0CgghhBBCCCGEEEIIIYQQJWDz5s28/fbbzJw5k0uXLtGuXTt69uxJSEjIA+MlJyczcuRInnvuuVJPo3QKCCGEEEIIIYQQQgghhKi4NNrye/1DX375JWPHjmXcuHHUr1+fr776imrVqrFy5coHxhs/fjxDhw6lTZs2/7aUHpl0CgghhBBCCCGEEEIIIYQQBmRnZ5OSkqL3ys7ONhg2JyeHCxcu0K1bN73j3bp14+TJk8V+x5o1awgICGD27NklmvbiSKeAEEIIIYQQQgghhBBCCGHAokWLsLW11XstWrTIYNi4uDjUajWurq56x11dXYmKijIYx9/fnw8++ICNGzdibGxc4uk3pGy+RQghhBBCCCGEEEIIIYT4NzTl99XTp09nypQpesdMTU0fGEehUOi912q1RY4BqNVqhg4dyty5c6lTp87jJ/YRSaeA+Ec0ReuuuM9VVV55J6HC81LLT8/DNFSllncSKrzjuXblnYQKz2vjp+WdhApNNWxaeSehwsvd/GV5J6HCU1SvWt5JqPAUnt7lnYSKLy6yvFMgngA+7WLLOwkVXl7SP18X+r/GJjSrvJNQ4U2IDCzvJFR4De1alHcShChRpqamD+0E+JuTkxNKpbLIrICYmJgiswcAUlNTOX/+PJcuXWLSpEkAaDQatFotxsbG7N+/n86dOz9+Ju4jT+aEEEIIIYQQQgghhBBCVFjaf7Hhb3kwMTGhefPmHDhwgP79++uOHzhwgL59+xYJb2Njw7Vr1/SOrVixgkOHDrF161Zq1qxZKumUTgEhhBBCCCGEEEIIIYQQogRMmTKFESNG0KJFC9q0acM333xDSEgIr7/+OpC/HFF4eDjr16/HyMiIRo0a6cV3cXHBzMysyPGSJJ0CQgghhBBCCCGEEEIIIUQJGDx4MPHx8cybN4/IyEgaNWrEH3/8QfXq1QGIjIwkJCSkXNMonQJCCCGEEEIIIYQQQgghKq5y3Gj435gwYQITJkww+NnatWsfGHfOnDnMmTOn5BNViFGpnl0IIYQQQgghhBBCCCGEEBWGzBQQQgghhBBCCCGEEEIIUWFVlo2GKwuZKSCEEEIIIYQQQgghhBBC/EfITAEhhBBCCCGEEEIIIYQQFVcl21OgopOZAkIIIYQQQgghhBBCCCHEf4R0CgghhBBCCCGEEEIIIYQQ/xGVvlMgKCgIhULB5cuXAThy5AgKhYKkpKRyTVdZ6tixI2+//XZ5J0MIIYQQQgghhBBCCCFKnFZTfq8nUbnuKRAaGsqcOXPYs2cPcXFxuLu7069fPz766CMcHR3/1Tnbtm1LZGQktra2JZrWw4cPM2/ePK5cuUJWVhYeHh60bduW77//HmPj8t2aYdu2bahUKt37GjVq8Pbbb1fIjoJG7w6g1rDOmNhaEn/pDudnrCXFL/yBcar2aknjaYOwqu5CWnAMVxdvIWzveb0w3qO6UP+N3pi72JHsF87FjzYQe/a23vdW79sGiyoOaHLUJFy7y9XFW4i/FGDwOzv8OI0qnZtwbMyXhO+98PgZL2Hd3x5I65c7Y2FrRfDlO/w66wei/cOKDd96SGdaDGiPW92qAIRdu8sfn/1MyJWC/Hs9XY9Orz1PVZ+a2Lo68MNrn3N9//niTlmhPP3OABoO64SprSXRlwI4+uFaEh5Sr2r1bEmr9wZiW92F5OAYTn/2C4GF6pVCacTTUwZQt19bLFzsSI9O4tYvxzi3dAdotRgZK2k1dSA1OjfFxtOZnNRMQv+6zqnFm0mPTirlHD8+h+G9cHp1AMYuDmT7hRC54Fsyzt0wGNbY2R63mWMxb+SNSY0qxK/bRdT8b+8LpMT5jUHYD3gOYzdHsgPDif5kDWnHLpZBbkpGi3cGUP9ePYq5FMBfH64l8SH1qGbPlrQsVI/OfvYLQff9Plm62dNq+hA8OzVGaWZCcmAUR6Z+S9y1IABeD/3R4LlPLdjEldW/l0jeSoNx4w4YN+uKwtIWbXwEOcd+QRNxp/gISmNUT/dGWe9pFBY2aNOSyD23B7XvSQAUDu6o2jyPkUt1jGwcyTm6hbzLh8ooN+Xr/OVrrPlpK7637hAbn8D/Fs3iufZtyztZZWLL1VDWXQwiLj2HWg6WvNe+Ls087IsNn5On4Zuzgfx+O5L49GxcrcwY27Im/Rp6ALDtehi7b0VyJz4NgPouNkxu400jt5K9LyxLm0/cYN2RK8SlZFDLzZ6pfdvSzMvdYNhZmw6z67xfkeNervZsm/YSADvO3mb25iNFwpxZPBZTVeXcbmzz0cusO3iOuOR0ark7MnVQJ5p5VzUYdtb6vew6XfTvnZe7I9tmjQYgV63mh31n2XX6BjFJadRwdeCtfu14pmHN0sxGmdt81o91x28Sl5ZJLWdbpvZsTrMaLgbDztp2il2X7xY57uVsy7bJvUs7qeVCyqco0259Me07BCM7R9Rhd8lcs5y8W9cMhlXW88Fi2GsYeXiiMDVDExtN9oGdZP++tVAgJWb9h2HSoTtGDs6oI0LI3PgNeZfPllGOSp7Z8/2wGDQEI0cH8oKCSF+5nNzrVw2GNW7og9Wr41FWyy8jdXQUWb/vInPbL7owJs+2w+Ll4SireKBQGqOOCCNj6xayD+4vqyyVOLuXe2M/diDGzg7k3AkmZuFqMi8YbodYdW2L3ZDemNavhcJERc6dYOKW/0jG8YI2hom3J05vjsCsYW1UHq7ELFxN4vrtZZSb0jP1g0mMHD0YWzsbLp6/wvvvzeP2rQfcawM2ttbMnPUOvZ/viq2dLSHBYcyeuZiDB44B8NaU1+j9fDdq1/YiMyuLc2cuMW/25wTcKfr7VRl0eHsAzYZ2xszWkvBLd9gzay2x/sW335xre9Dx3YG4N6qJXTVn9s3dwJkf9hY5Z4d3XtQ7lhaTxJctJ5ZKHoQoT+V25x8YGEibNm2oU6cOmzZtombNmty4cYOpU6eyZ88eTp8+jYODwz8+r4mJCW5ubiWa1hs3btCzZ0/efPNNli1bhrm5Of7+/mzduhWNpvy6i3Jzc1GpVP+qnMpD/Yl9qPdaL06/vYrUwCgavt2PTj9P5/d275GXnmUwjmNzb55ZNZlrn24ldO85qvVoyTOrJ3Ow3zzdA33PF1rTbO4Izs9YQ9xZP7xHdKbDxmn80XEaGeHxAKQGRnF+5lrSgmNQmplQ77WedNz0AbvbTiE7IVXvO+u+2gO02tItjMfQ+fUX6DC2F5veW0ns3Ui6Th7A6z/OYHHnKWQXU461Wjfg4s4TBF30Iy87l07jn2f8hhl82vU9kqMTATCxMCPiZjBnfznCK6vfLcssPZZmb/Sh6as9OThlNUl3o2jxZl/6/vQBP3aYSm4x5eHWzJvuKyZx5vOtBOw9T60eLei+YhLbBswn+nJ+vWo2oQ+Nhj/HwXdWk+AXhkvjmjz3xWtkp2Zy9Yd9GJub4NyoBuf+t5043xBMbS1oN2cEvX+YwpbeH5VlEfxjNr3b4fbhq0R+tJKMC77YD+1J9R/mcKf7BHIjYouEV5ioyItPIfbrLTiO6WvwnK7vjsCubyfCZywjOyAU6/bN8Fw1k8CBU8nyDSztLD22pm/0ofGrPTl8rx41f7MvfX76gJ8fUI9cm3nTdcUkzn2+lbt7z1OzRwu6rpjEjgHziblXj0xsLei37SPCT93kj5GfkRmXgk11V3JSMnTnWddM/wbTs1MTOn42jsA9FbdBrKzdHFX7QeQc3oQmIgBjn3aY9p1E1o9z0aYmGoxj0vNVFBbW5BzcgDYpFoWFNRgVTFhUqEzQJseR638Rk/aDyiorFUJmZhZ1vb3o16sb78xcUN7JKTP7/KL47NhtpnesR9Mqdvx6PZxJOy/x6/A2uFubG4wzbc9VEjJymP1cAzztLEjIyCGv0N/s8+GJ9KjjRhN3O0yURqy7GMQb2y/y6/A2uFiZlVXWSsy+S3f4bMdJZgx4lqY13dh6ypeJ3/7Btmkv4W5vXST8tH5teat3K917tUbDS19spWsTL71wVmYmbH9/sN6xytohsO/8LT7bepgZQ56jqZcHW49fZeLX29g2azTuDjZFwk8b1Im3+rbTvVdrNLy0cD1dn6qjO/b1zhP8fvYmHw3rSk03B076BjHlm52se28I9aq5lkm+Stu+a8F8tuciM/q0oKmnM1vP3WHij0fYNqk37naWRcJP69Wct7o21b1Xa7S8tOIPujasVoapLjtSPkWp2nbC/JVJZHz7FXm3r2Ha9QWsZn5K8juj0MbFFI2QlUnW3t9QBwdCdhbG9XyweG0K2uwscg7uBsB8yFhM2nclfdXnaMJDMG7aEqup80mdORF10IMfflZEph06YfXGJNKWLSH3xnXMej+P7cJPSBg7Ck2soTLKInPHb+QFBqDNykLVyAfrt95Fm5VF1h+7ANCmpJLx04+oQ0PQ5uZi2roN1u+9jyYpkdzz58o4h4/Pumd7XKaPJ3re12Re9MV2cC+qfjOfu33GkxdZtB1i3sKHjJOXiFuyDnVqGrYDulJ1xRyCB79D9s38+20jMzNyQ6NI3Xsclw9eK+sslYrJb7/KGxNfYfKEDwi4E8SUqW+wdfsaWrfoQXpausE4KpWKrdvXEBcbz5iRbxEREYWHhztpaWm6MG2feZofvt3IpYvXMDZWMmPWO/zy2/c826o3GRmZZZW9EtH29T60HteLHe+tIj4winaT+zF843S+7vQeOcW031TmpiSGxOD7+xm6fTS82HPH3A5lw7BFuvda9RM6TLwykv+KElVuywdNnDgRExMT9u/fT4cOHfD09KRnz54cPHiQ8PBwZs6cCeSPel+4cCFjxozB2toaT09Pvvnmm2LPe//yQWvXrsXOzo59+/ZRv359rKys6NGjB5GRkXrx1qxZQ/369TEzM6NevXqsWLFC99mBAwdwd3fn008/pVGjRtSqVYsePXrw3XffYWJiogt38uRJ2rdvj7m5OdWqVePNN98kPb3gBzs7O5tp06ZRrVo1TE1NqV27Nt9//71eOgvbvn07CoVC937OnDk0bdqUH374AS8vL0xNTdFqtXrLB3Xs2JHg4GDeeecdFAoFCoWC9PR0bGxs2Lp1q975d+3ahaWlJamp+g/FS0vdcT24sXQ7YXvOk3w7jNNvrcLY3ITq/YsfDVn31Z5EHbuO7/KdpN6JxHf5TqKP38h/cP93mNd6ErjpCIE/HSHlTgQXZ/9IRkQ8tUd20YUJ/u0k0X/dID0klhS/cC7O2YiJjQV2DTz1vs+ugSd1x/fizJTi61h5az+mJwe/3s61feeI8gvjp3dXYGJuSrO+zxQbZ+Pbyzn54wEifIOJCYhgywffoFAoqP1MI12YW0cus+eLLVzbV7luLpuM7cH5ZTsI3HuehNthHHxnNcZmJtTpV3y9ajKuB6F/XefC17tICojkwte7CDvhS5NxBfXKvVlt7u6/QPChy6SGxRHwxzlCj13DpXH+KMGc1Ex2DvuEO7vPkBQYSfSlAI59tB6Xxl5YVfl3M53KitPYfiT+coDELfvJDggjav635EbG4TCsl8HwueExRM3/hqTfDqFJzTAYxq5fJ2JXbiHtyHlyQ6NJ2LiHtGMXcRrXvzSzUmJ8xvbg4rId3N17nsTbYRy6V4+8H1CPfMb1IOyv61y6V48ufb2L8BO++BSqR0+98TxpkQkcefcbYi4HkhoWR/iJG6QEFzQMM2OT9V41ujUj/ORNUkOKNowqCuNmXci7cQL1jRNoE6PIPfYL2rREjH06GAxvVL0Byqq1yd6xHE3oLbSp8Wiig9BEFnQYaaKDyT2+DbXfebTqvLLKSoXQrk1L3nxtFF07Fv87/iT68VIw/Rp6MKBRVbwcrJjavi5uVmb8ctXwzLcTQXFcCE9kWd+naO3pSBUbcxq52dLU3U4XZmF3H15qXI26ztbUdLBkVucGaLVazoQmlFGuStaGY9fo/3Q9BrSuj5erPdP6PYObnRW/nPQ1GN7a3BQnGwvd60ZoLCmZ2fRtWbdI2MLhnGwsSjsrpWbDoQv0b+vDgGca4+XuyLRBnXCzs+aXY1cMhrc2N8XJ1lL3uhEcRUpGFn3bFNwT/X7Wl7E9nqZdIy+qOtnxUvumtKlfnfUHK97s0X9rw8lb9G/mxYDm3ng52zKtV3PcbCz45Zy/wfDWZiY4WZvrXjci4knJyqFvs1plnPKyIeVTlFmfQeQc+oOcQ7+jCQ8hc+1yNHExmHYzPGBEHXSH3BOH0IQFoYmNIuevA+ReOYdx/ca6MCbtu5G1bSN5l86giYkkZ/9Oci+fw/T5wQbPWdGZv/gSWXv/IGvP76hDgklfuRx1bCzmzxsuo7wAf7IP/4k6OAhNdBTZfx4g58I5VD4FZZR79TI5J/5CHRKMJjKCzN9+JS8wEFVDn7LKVomyH92f5F/3k7x1HzmBocQuWk1uVCx2LxueURO7aDUJ328l67ofucERxC1ZR05wBFadCjrAs677EfvZ96T+cRRtbm5ZZaVUjX9jJEu+WMXvuw5w66Y/k15/H3NzM14c1KfYOENHvIidvS0jh07k7JmLhIVGcOb0BW5cL1hBYfCL4/j5p9+4fesON67f5s0J06nm6UGTpg3LIlslqtXYHvy1fDu39p4n1i+MHe+uQmVmQqO+xbffIq4GcnDhJm7sOo06u/j2hiZPQ3pssu6VkVA2z8yEKGvl0imQkJDAvn37mDBhAubm+iPB3NzcGDZsGJs3b0Z7b+TXF198QYsWLbh06RITJkzgjTfe4NatW4/8fRkZGXz++eds2LCBY8eOERISwnvvvaf7/Ntvv2XmzJl8/PHH3Lx5k4ULFzJr1izWrVunS1NkZCTHjh0r9juuXbtG9+7dGTBgAFevXmXz5s0cP36cSZMm6cKMHDmSn3/+maVLl3Lz5k1WrVqFlZXVI+cD4M6dO2zZsoVff/1Vt49CYdu2baNq1arMmzePyMhIIiMjsbS0ZMiQIaxZs0Yv7Jo1axg4cCDW1kVHmpU0S09nzF3tiTpaML1Uk5NHzOlbOLeoXWw8p+beRB3Vn24ZeeQqTi3yR3MZqZQ4NK6pd16AqKPXcCrmvEYqJd7DO5GTnE6ib7DuuNLchLYrJnFh5jqyYpP/cR7LgkM1F2xc7Ln9V0GZqHPyCDhzkxrN6zwgpj4Tc1OUKmMykgyPMqgsbDydsXS1I+SYfr0KP3ML9+bF1yu3Zt56cQBCjl7FrVCciHN+VH2mIXY182ceOdb3xL1lXYIPG37IAGBibY5WoyE7xfCD84pAoTLGvJE3aX9d0jue9tclLJrV+/fnNVGhzc7RO6bJzsGiRYN/fc6yYn2vHoXeV48iztzSqxP3c23mTdh99SjsvnpUvWszYq8G0nXlZEZd+pqBexZQ/+WOxZ7T3MkGz85NuWVgaY8Kw0iJkYsnmpCbeofVwTcxcvcyGEXp1QRNdDCqFt0wG7sYs5FzUT37IihVBsOLJ1+uWsPNmFTaeOp3orb2dOBKZJLBOEfvxtLA1Ya1F4Lo9v0x+q4/wZd/+ZGVpy72e7Ly1ORptNiaVb66lpun5mZYLG3q6i+D07puVa4ERT/SObafvUWr2lWp4qB/r5eZk0vPBRvpNu9HJn+3h1thcSWW7rKUm6fmZkg0bepX1zveun51rgRGPNI5tp+8Tqu61aniWDCrICdPjel9y4OamRhzKeDBS8pVFrl5am5GJtCmlv4yVK293bgS8mh1YfuFAFp5uVHFwKj5yk7KxwBjY5Redcm9oj94KPfqOYzrPtrDRGUNb4zrNiLvRqF7aZUKba7+/SM52RjXq4QPvI2NMa5Th5wL+mWUc+Ecxg0bFRPpvlPUqo2qQUNyr14uNozqqWYYV61G7jXDSxJVaCpjzBrWJv2E/vKiGScuYv7UI7YZFAqMLM1RJz+5D2mr16iKq5sLRw4d1x3Lycnl5IlzPP30U8XG69GzM+fPXuaTLz7ihv8Jjp3axdvvjsfIqPjHfja2+fcHiYkV8/lHceyqOWPtYk/gXwVtMXVOHsFnblHtAe23R+VQ05V3zi5n8vElDFg2Cbtqzo99TiEqonKZJ+zv749Wq6V+/foGP69fvz6JiYnExuaPkuzVqxcTJkwA4P3332fJkiUcOXKEevUe7QFWbm4uq1atolat/JEakyZNYt68ebrP58+fzxdffMGAAQMAqFmzJr6+vqxevZpRo0YxaNAg9u3bR4cOHXBzc6N169Y899xzjBw5Ehub/AbEZ599xtChQ3Uj9mvXrs3SpUvp0KEDK1euJCQkhC1btnDgwAG6dMkfwe7lZfjhyYPk5OSwYcMGnJ0N/yg5ODigVCqxtrbWW0Zp3LhxtG3bloiICKpUqUJcXBy7d+/mwIED/zgN/4a5ix1AkYftWbHJWFZ1KjaembMdWXEp+nHiUjBzzl8b2NTBGiNjJVlxRc9r5qK/fnCVLk/RduUkjM1NyIxO4vCQxeQkFEylazZnOHHn/QjfV3FHgdk42wGQel85psYmY/+Acrxf7/dfJjkqAb8ThtcArSws7pVH5n3//5mxyVg/oDwsnO2KxolLxtK5oM5cXLELU2tzhh35FI1ag5HSiNOf/oL/jlMGz6k0VdF2+mD8tp8iN63iTr1U2tugMFaSF6e/xIs6PhFj52b/+rxpf13EcUw/0s/eICc4EstnmmDTpRUYKR83yaXucepRxn1xMuKSsShUj2w8nWkw/DmufreXi8t34tK0Fs/MG4k6Jw+/X4/ff0rqDmxHbnoWd/dU3P08FOZWKIyUaDP0f5u1mSkoLIsu1QFgZOOEURVvUOeSs3sVmFth0ullFGYW5BzcUBbJFhVMYmYOaq0WBwsTveOOFqbEZ8QbjBOenMnliCRMlUZ82bsJiVk5LDp8i5TsXOZ0MfxQaukJf1ysTGlVrXIstVhYYnoWao0WByv9ATSOVubEFTNrq7DYlHRO3Apl4bDn9I7XdLVj3pCOeLs7kp6Vw09/XWP08h1sfncg1Z0r194LiWmZ+WVkrT/TwdHGkriUoIfGj01O44TvXRa+oj9CtU39Gmw4dIFmtatSzcmOM7eDOXIlAHUFXl7yn0jMyL5Xt/SX1HK0NCcuLbKYWAViUzM5cSeShQOfzL1PpHyKUljbolAq0STp3z9qkxIxsnvw76vtql9Q2NiCUknWlrXkHCrYLynvyjnM+gwiz/cKmugIjH2aoWr5jN7ygpWFka0tCqUxmkT9mWnaxESM7B9cRg4//YKRrR0olWRsWEvWHv09pRQWljj+vBVUJqBRk7b0K3IvVtx7xeLo2iHx+vUoLz4JS6fi9xMqzP6VARhZmJG6p/gBm5Wdi0v+s56YGP37odjYOKpVq1JsvOo1qvFs+9b8+ssuXh70Gl61qvPJ5x+hVBrzxadfG4wz7+PpnD55nls3Dc+Cqqis7j1fSrvvuUhaXDJ2Ho/+XMSQ8MsBbJ+yioTAKCydbGg3uR9jts1hZdf3yUxKe/gJRKl6Ujf8LS8VcvHQv2cI/L10TuPGBdPnFAoFbm5uxMQYWJOvGBYWFroOAQB3d3dd/NjYWEJDQxk7diyvvvqqLkxeXp5us2KlUsmaNWtYsGABhw4d4vTp03z88cd88sknnD17Fnd3dy5cuMCdO3fYuHGjXj40Gg13797l2rVrKJVKOnQwvLTCo6pevXqxHQIP8vTTT9OwYUPWr1/PBx98wIYNG/D09KR9+/bFxsnOziY7O1vvWK5WjUrx8Id81fu3peWnY3Xvj474DDCwVL9C8fD1+w19ft8x7f1hFAq471D0CV/2dp2BqYM1tYZ14pnVk9nfezbZ8Sl4dGuG6zMN2dttxoPTUsaa9X2GQQsL6uV3Yz4BiuZXoeCR90HoNP55mr3wDF8PmUdeduWaXlmnX1s6Lh6je7979OeA4Xr1z6uVQq9ca7/QmjoDnmH/5BUk+IXh1KA67eYMz99weOtfejGNjJV0/3oiKIw4MnPtP8tUeTGQ/8fZSyNy3jd4LJxM7QMrQQs5IZEkbj2I/cAuD49cxmr3a0v7QvXoj3v16P4yUTxCPSoS5756pDAyIvZqIGc/2QJA/I1gHOp40GDEc4Y7BQZ3wP+3k6grw7VZ9MIzUK/+/kgBaMne+wPk5K/xmXvsF0x6vwaHfwZ1JcivKBWK+95r0aK4/+A9Gq0WBfBx90ZYm+aP/M9pp2HqH1f5oGM9zIz170/WXghir18U377YAlPjit9BWZz7y0NL/m/Nw+w854e1mSmdG9XQO964uiuNqxesi9+0hhtDlvzKz8ev837/yrmEleK+QtJqi69Hhe08dQNrc1M6N/HWOz5tUCfmbdxP/7lrUCigqpMdL7RpyM5ThjfCrKwMX3+PULcuBWJtZkLneoY3c35SSPkY8vA21/1SP5oMZuYY126A+bDXUEeFk3viEAAZa5ZhMX4qNv9bD1rQRIeTfXgPpp16llL6y4CB26OH3VAmTZmMwswCVf0GWI57DXVEONmH/yw4ZWYGCa+PQ2FujslTzbB8fQLqyIgHziio0AzcQxZpzxtg3bsDTpOGEz5xLuqEyjWy/UFeHPQ8X3w1V/d+6Evj8/9RpM3/4LaJkZGCuNh4prw5C41Gw9XLN3Bzc2HSm2MNdgp88vlHNGhYhz49hpZIPkpTo35t6bOw4PnSplc+Mxguv4werwP/zpFCs5luQ9jFO0w+9iVNBrbj9Hd7HuvcQlQ05dIp4O3tjUKhwNfXl379+hX5/NatW9jb2+PklN/Dp1LpT/lWKBT/aINfQ/H//qH4+zzffvstrVq10gunVOo3ID08PBgxYgQjRoxgwYIF1KlTh1WrVjF37lw0Gg3jx4/nzTffLPL9np6e3Lnz4I2SjIyMivx45RpYD8/S8t9PQR03bhzLly/ngw8+YM2aNbzyyisPvLFdtGgRc+fO1Ts2wKoRA60bFxOjQPj+i7qNgAGMTPKrmrmLLVkxSbrjZk42D1yqJys2STcrQC/OvdkD2QmpaPLUmN8b6fug86ozs0kLiiYtKJr4i3foc/wLar3cEd/lO3F9pgFWNVx48da3enGe/fZtYs/c4tDAjx+a59Jw4+AFQi4X1B2lSX5dtnGxIzU2SXfcysmW1LiH3xh1fLUPXSb2Y+Wwj4m8FVLi6S1tdw9c1G0EDKC8V68snG3JKFSvzJ1syHxAvcqITdIbzf13nIxCs1LaznyZiyt24b/zNADxt8KwrupE84nP63UKGBkr6b5yMjbVnNk+eFGFniUAoE5MQZunxthZfzSO0tGOvLikf3/ehBRCXv8YhYkKpb0NedHxuL4/mpzQR1vioiwFFVOPzO+rR2b/sh5lFqpHGTFJJPrrL2GReCcCr14ti5zP7em62HtX4eCE5f8oP2VNm5mGVqNGYamfd4W5dZHZA7o46clo05J0HQIAmoQoFAojFNb2aJMevaNfPBnszU1QKhTEZ+gvG5GQkYODuYnBOE6WprhYmeo6BABqOliiBaLTsqheaJmO9ReD+P7cXVb1b0Ydp9JfJrE02FuaoTRSEJ+q/3clIS0Tx2I2Yv6bVqtl+9lb9G5RG9VDOkSMjBQ0rOZMyCPcR1Q09lbm+WWUor8cYkJqBo7WD75n1mq1bD91nd6tGhQpIwdrC756vR/ZuXkkpWfiYmvF/7b/RRWnyjWTojj2Fqb55ZamvxFjQnoWjpYP3pBbq9Wy/WIAvZvUeGjdqqykfIrSpiajVasxsnOg8IJtCls7NMkP3rNFExMFQE7IXYxsHTB/abSuU0Cbkkz6Zx+CygSFtQ3ahDjMh72GJubhMzIqGk1yMlp1HkYO+rMCFHb2RWZYFIkblV9G6qBAjOztsRgxWq9TAK0WTUT+8mWZAXdQelbH4uVhJFeyTgFdO8RJv4yMHW1Rxyc9MK51z/a4LXibiLcXknHqcuklshzs3XOIixcKHkT/vW+li6sT0dEFe4w5OTkSG1P8EmbRUbHk5uXpPSvzvx2Iq5sLKpVK7/nSok8/pHvPzrzQaziRERWvvXY/vwMXWV3o+ZLxvfablbMtaYXab5aONqSX8P1MbmY2MbdDcajh9vDAovTJTIESVS7z8hwdHenatSsrVqwgM1O/oRMVFcXGjRsZPHjwI43EeFyurq54eHgQGBiIt7e33qtmzZrFxrO3t8fd3V23kXCzZs24ceNGkXN4e3tjYmKCj48PGo2Go0ePGjyfs7MzqampehsTG9oz4FGYmJigVhddX3f48OGEhISwdOlSbty4wahRox54nunTp5OcnKz36mv1aGtG5qVn6R7ApwVFk+IXTmZ0Im7tC9aHNFIpcWldj9jzxU9Vi7twRy8OgFuHxsSd9wNAk6sm4epd3Nrrr9Po1t6HuAecFwAFGJnm/zHxXb6LPc9NZ2/XGboXwKU5P3LmnfLbdDg7PYu44GjdK9o/jJSYROo8W1AmSpWSWq3qE3TB74Hn6vRaH7pOHsA3oxYRdi3wgWErqtz0LJKDonWvBL9w0qOTqNau4P/fSKXEo1U9Ii8U//8fdfGOXhwAz/Y+RBWKozI3Qau5b0aKWoPCqOB36e8OAbuarmx/eTFZlWA6oTY3j8zrd7B6tqnecatnm5Jx8dH3ain2/Dm55EXHg7ESm+5tST145rHPWdJy07NICYrWvRKLqUdVWtXTqxP3i754h6r31aOq99WjqPN+2N23JrGdlxupBtbvrj+kAzFXA4m/WcE77DRqNDEhGHnqLwGo9Kyvt3FwYerIABSWdqAy1R0zsndFq9GgTX1wQ1k8mVRKI+q7WHM6RH9q/OmQBJoU2ji4sKZV7IhNzyYjp2BjuODEDIwU4FpomY91F4L49uxdvu77FA1dK+9DXJWxkvpVnTnlp7/x8hm/MJrUcC0mVr7zAZGExqXQ/+mHL7Wp1Wq5HRGPk3Xl22xYZaykvqcrp24G6x0/cyuYJl7FL7EAcN4/jNDYJPq3LX7tclOVMa521uRpNPx52Z+OjZ+MTWNVxkrquztwKiBK7/iZgCiaeD542YXzQTGEJqTR/wnaQPd+Uj4G5OWhDryNceMWeodVjVuQd/sfzKBRAMYGOn5zc9AmxIFSiap1B3LOnXi89JaHvDzy/PwwaaZfRibNWpB34/qjn0ehQKF62D44CnhomAooN4+sG/5YtNVfF9+ibTMyL/kWG826dwfcFk0h8r1PST96rthwlVV6Wjp3A0N0r9u37hAdFUOHTgWz91QqFW2facnZs5eKPc/ZMxepWdNT7zlaLe8aREXG6HUILP5sFr2f78aA50cREhxm6FQVTk56FonB0bpXrH84qTGJeD2r/3ypeqt6hD6g/fZvKE2McfL20Ot8EOJJUW7LBy1fvpy2bdvSvXt3FixYQM2aNblx4wZTp07Fw8ODjz8uu5HZc+bM4c0338TGxoaePXuSnZ3N+fPnSUxMZMqUKaxevZrLly/Tv39/atWqRVZWFuvXr+fGjRssW7YMyN/roHXr1kycOJFXX30VS0tLbt68yYEDB1i2bBk1atRg1KhRjBkzhqVLl9KkSROCg4OJiYnhpZdeolWrVlhYWDBjxgwmT57M2bNnWbt27b/KT40aNTh27BhDhgzB1NRUN+PC3t6eAQMGMHXqVLp160bVqg+e0mpqaoqpqanesUdZOqg4t7/bS4PJL5AaGEXq3SgavNmXvMwcgn87qQvT+n+vkxmVyJVFmwHw+24vz22bRf2JfQjbd4Gq3Zvj1q4hB/sV7Alx+5s9tF76BglX7xJ33p9awztj4eGI//r80RVKc1MavtWX8P0XyYxOwtTBitqjumDh7kDIrvwHllmxyQZnLKSHx5EeGlvkeHk69sMeukzsR1xQFLF3I+kysT85mdlc3FFw8/zyFxNIiU7g909/BvKXDOo55SV+fGsZCWGxWN8b3ZydnkVORv4SUSYWpjgV6v12qOZClQbVyUhKIynC8PrOFcGV7/fSYtILJAdFk3Q3ihaTXiAvKwe/7QX1qsuS8aRHJXLq3hIuV77fx4CtH9LsjT4E7r+AV7fmVH22IdsGzNfFuXvwEi0m9yU1PJ4EvzCcG9Wg6as98d2c37GnUBrRY/WbODeqwe7RX2CkNNKNGs9KSkOTW/zGl+Ut7vvtVP1iCpnX7pB58Sb2L/dAVcWZhI1/AOA6dRTGro6Ev/elLo5Z/fxOUiNLM4wdbDGrXxNtbh7Zd0IBMG9SB5WbI5m+gajcnHB5a2j+0jmrfy37DP4L177fy1OTXiApKJrku1E0u1eP7hSqR53u1aO/lwK69v0++m79kKZv9CFo/wVqdGuOx7MN2VGoHl39bi/9fvuIpya9QMDuM7g09aL+0E4ce/8Hve9XWZnj1ftpTs3/qWwy/JjyLh7EpPsraKKD0UQGYuzTDoW1PXnX8td3VbXth8LKjpz9awFQ3z6H9ulemHQdSe7p3SjMLFE9OwC178mCpYOMlCgc3Av+bWWHwqkq5GajTa5Yv8MlLSMjk5Cwghkl4RHR3PILwNbGGnc3l3JMWeka/lR1Ptx/nQYuNjR2t2Xb9XCi0rIY6JN/f7L0hD8x6dks6Jbf+dazjhvfng1k9sEbvN6qFklZuXx1wp++DTx0SwetvRDEilN3WNjDhyo25sSl5/+Ns1ApsTCpkKtmPtCI9j7M3HSYhlWdaVzDlV9P3yQyMY2BbfI3ZFz6+xliktNZMLSzXrztZ2/h4+mCt3vRdaxX7TtP4+queDrbkpaVw6a/ruMXHs/0Ac+WSZ5K2ojOzZm5bg8Nq7vSuGYVfj1xlcjEVAa2awLA0u1/EZOUxoLR+suRbD95DZ8a7nhXKfqQ99rdSGKS0qhbzZmYpDRW/X4KjUbL6K5FZ3lVViPa1mPmtlM09HCgcTUnfj1/h8jkDAa2zN+gcemBy8SkZLDgRf118bdfDMCnqiPernblkOqyI+VTVNbuX7CcPAN1wG3y/G5g2uV5jJxcydm/EwCzoa9i5OBExvJFAJh274cmLhp1eP5gB+N6Ppi9MJisPb/pzqn0ro+RgxPqoDsYOThh9tJoUCjI3vFzmeevJGT+ugXr92eS53eb3Js3MOvVB6WLC5m788vIcsyrGDk5k/rpQgDMXuiHJiYGdWh+x6Zxo8aYDxpM5vZtunOaDxlGnt9t1BHhKFQqTJ5ujVnX7qQt/bJoAiqBxLW/4f7Je2Rd9yfr8k1sX+qJyt2ZpJ/z2yFOU0Zj7OJI1AdfAPkdAu6L3yNm4Soyr9xCeW/vAW1WNpq0e/vrqIwxreUJgEJljLGrI6b1vNBkZJIbUvlmnQCsXrmet6eMJzAgiMCAYN5+dzyZmVn8+stuXZjlqz4hKjKaBXPz68Ka7zcx7rURLPxkJt+u/pFatarz1rvj+W51wf5dn3wxmxcH9mHk0AmkpaXj4pL/NzAlJZWsLP2loyu6M9/v5dmJLxAfFEXC3SiendSX3Kwcru8oaL/1/fJ1UqMSOfRp/vMlI5US59r595lKE2Os3exxbVBd1+kA0HXmUPwOXiQ5Ih5Lx/w9BUytzLny619FEyHKnOwpULLKrXVUu3Ztzp8/z5w5cxg8eDDx8fG4ubnRr18/Zs+ejYND2W0IN27cOCwsLPjss8+YNm0alpaW+Pj46DYNfvrppzl+/Divv/46ERERWFlZ0bBhQ7Zv367bI6Bx48YcPXqUmTNn0q5dO7RaLbVq1WLw4MG671m5ciUzZsxgwoQJxMfH4+npyYwZ+SPSHRwc+PHHH5k6dSrffPMNXbp0Yc6cObz22mv/OD/z5s1j/Pjx1KpVi+zsbL1licaOHctPP/3EmDFjHnCG0nHz690ozUxosWg0JraWxF8K4MjLi8lLL5iaa+HhqDc6O+68PyffWE7j9wfhM3UQacHRnHh9md7SRCE7T2Nib0XDd/pj7mJH8u0wjg7/jIzw/JG4Wo0GG+8q1BzUDlMHa7IT00i4EsjB/vNJ8QsvuwIoIYdW7URlZsKL88dgbmtJyOU7rB6xkOxC5Wjv4aT3//7MiG4Ym6oYvWqK3rn2fbWVfV9tBaBa41pM/Pkj3Wf9Zo0E4OzWo/z83srSzNJjubhyN8ZmJnRYMBpTWwuiLwewY9gn5BYqD+v7yiPqgj/7Ji6n9dRBtHpvIMnB0eybsFxvSZljs9bT6r2BdPh4NBZONqRHJ3J94yHOfZXfkLFyd8CrW3MAXt6/UC9Nvw36mPDTN0sz248l5fe/iLK3xmXyEIydHcj2CyZ4zBxyI/IfvBo722NSRX/vEu/fl+n+be5TG7u+HckJi8avff7ajgpTE1ymjMDE0w1NeiapRy4QNuULNKn6SzpUVJfv1aN29+pRzOUAdhuoR4UX8oy+4M/BictpOXUQLd8bSEpwNAcnLCemUD2KvRLIvle/otUHg2n+Vj9SQ2M5OedH/At1NgB4v9AaFAruFLORdUWj9r9ArrkVqla9UVjYoI2PIHvHcrSp+UsIKCxtUVgX+juem032b//DpOMQzIZMR5uVln+Okzt1QRSWdpgP+1D33qh5N1TNu6EO8yP718rZ8H1U12/5M2by+7r3ny7Ln6HWt2cXPv7w3fJKVqnrXseN5KxcvjkbSFx6Nt6OVix74Smq2OQvjROXkU1UaqF7BBNjVvZrzidHbzF88xlszVR0re3KxDYF68FvuRpKrkbL1D+u6n3X+Ke9eL115Ru52/0pb5Iysll94AJxKRl4uzuwfFxPqjjkL4kUm5JB5H2z1FIzs/nz6l2m9jO8yWlqVg7ztx4jLiUDK3MT6lVx4vuJz+PjWTk7oLq3qEdSehar/zhNXEo63u6OLJ8wgCqO+Rufx6akE5mov7RZamY2f17yZ+qgTgbPmZ2bx9e7jhMWl4yFqYpnG3qxYFRPbCwevHRMZdLdpzpJmdmsPnKduNRMvF1sWT68I1XuLcMVm5pJZLL+htapWTn86RvK1J7NyyPJZUrKp6jck4fJtLLBbOAojOwdUIfeJW3h+2ji8h+kGdk7YuRUaBaTQoH50NcwcnEDjRp1VAQZG78h58CugiAmJpi/PBYjlyposzLJvXSa9GUL0WZU/Nm3hmQfPYzCxhaL4SMxcnAkL+guyTPfRxNzr4wcHTFyKfRbqzDCcsyrKN3c0WrUqCMiSP/uG7J+L3R/ZGaG1ZvvoHRyRpudjTo0hNTFC8g+eriss1ciUvccQ2lnjdPEoSidHcjxDyJs/EfkReQvJWns7ICqSkEZ2Q3uhUJljOvsSbjOnqQ7nvzbAaKm598fGrs4UGN7wZr5DmMH4jB2IBlnrxI6suD+qjJZ9tW3mJmZ8ukXs7G1s+Xi+SsM6j+G9LSCtlXVqu5oCy0VFBEexaD+Y5i/aDpHT+4kMjKab1etZ+mSgiWSx4zL3z9gxx8/6n3f5Dc+4OeffqMyOblqNyozE3otGI25jSXhlwP4cfhicgq132yr6D9fsna1Z/yegrZ72/F9aDu+D0GnfFk/JH9gsrWbAwOWTcLC3pr0hBTCL93h+/6zSQ4vfukmISorhfZxd+EQlcrGjRt56623iIiI0K1V909sqjKsFFL1ZDlnUnFHiFcUXurKN1qzrHVUJZV3Eiq847l25Z2ECm/ke1blnYQKTTVsWnknocLL3fxkd8iUBEV1r/JOQsVnLr9FDxVXOUezioola2vlfFBclvKS5PHHwySEVr7l5Mras8UsmSkKTLZr8fBA/3EfBW8s7yRUOrFdO5TbdzsfMLwcfGUmT+b+IzIyMrh79y6LFi1i/Pjx/6pDQAghhBBCCCGEEEIIIcqaLB9Ussplo2FR9j799FOaNm2Kq6sr06dPL+/kCCGEEEIIIYQQQgghhCgH0inwHzFnzhxyc3P5888/sbKSKdxCCCGEEEIIIYQQQojKQaspv9eTSDoFhBBCCCGEEEIIIYQQQoj/COkUEEIIIYQQQgghhBBCCCH+I2SjYSGEEEIIIYQQQgghhBAVl1ZR3il4oshMASGEEEIIIYQQQgghhBDiP0JmCgghhBBCCCGEEEIIIYSosJ7UDX/Li8wUEEIIIYQQQgghhBBCCCH+I2SmgBBCCCGEEEIIIYQQQogKS6uRPQVKkswUEEIIIYQQQgghhBBCCCH+I6RTQAghhBBCCCGEEEIIIYT4j5Dlg4QQQgghhBBCCCGEEEJUWLLRcMmSTgHxj6i05Z2Cis9eqyzvJFR4DXJyyjsJFV5YjlV5J6HCSzQr7xRUfApLi/JOQoWWu/nL8k5ChacaPKW8k1Dh5f74SXknoeJTyr3RQ6nV5Z2Cik8h6wg/zLG/qpR3Eiq8plViyjsJFd7lZIfyTkKFl5h5tbyTUOH9aHK7vJNQ4X1U3gkQ/3nSKSCEEEIIIYQQQgghhBCiwtJqZYBASZI9BYQQQgghhBBCCCGEEEKI/wjpFBBCCCGEEEIIIYQQQggh/iNk+SAhhBBCCCGEEEIIIYQQFZZsNFyyZKaAEEIIIYQQQgghhBBCCPEfITMFhBBCCCGEEEIIIYQQQlRYWo1sNFySZKaAEEIIIYQQQgghhBBCCPEfITMFhBBCCCGEEEIIIYQQQlRYWm15p+DJIjMFhBBCCCGEEEIIIYQQQoj/COkUEEIIIYQQQgghhBBCCCH+I57YToGgoCAUCgWXL18G4MiRIygUCpKSkso1Xf9GZU67EEIIIYQQQgghhBBCPA6tRlFurydRhdxTIDQ0lDlz5rBnzx7i4uJwd3enX79+fPTRRzg6Ov6rc7Zt25bIyEhsbW1LNK0KRUHFUCqVVKlShYEDB7Jo0SJMTU1L9LueNF6julB3Qm/MXOxI8QvnykcbiDtzu9jwTm3q0WTOcGzqeJAZnYTfit0Erv9T97lNHQ8aTBuIfeOaWFZz5vJHG7jz7V69c9Sd/AIevVpg7V0FdVYO8ef9ubbgZ9ICIkstn6Wh/dsDaDa0M2a2loRfusPeWWuJ9Q8vNrxzbQ86vDsQ90Y1savmzL65Gzj7w95iwz8z4QU6vz+YM9/vYf+8H0sjCyXGY3Q3PCe+gImLHem3w/CftZbkM7eKDW/Xpj7ec0dhWbcqOdGJBC/fScT6A7rPFcZKqr/ZD/fBHTBxcyAjIIKA+RtJOHyl4Byt6+M58QWsG9fE1M2Bq6M/I27PuVLN5+OoNrorNSc+j6mLHWm3w7g1az2JDygj+zb1qTd3BFZ1q5Idncjd5bsIXX9QL4yxjQW1pw/GtffTqGwtyQyJ5dacDcT9eTn/O0d1xXN0F8yrOQOQdjuMO19sI+7Q5dLKZql49u0BNB3aCTNbSyIuBbB/1lriHnCtOdX2oN27L+J271o7OHcD537YpxfmqeHP0Wz4c9hWzS+bOP8wjv/vNwKPXC3VvJS0zZeCWHcukLi0bGo5WTG1c0OaVXUoNnxOnprVp/z5wzeCuPRsXK3MGNfGm34+1QDYcT2U2XuKlsGZd3pgaqwstXyUpi1XQ1l3MYi49BxqOVjyXvu6NPOwLzZ8Tp6Gb84G8vvtSOLvldHYljXp19ADgG3Xw9h9K5I78WkA1HexYXIbbxq5ley9TUV0/vI11vy0Fd9bd4iNT+B/i2bxXPu25Z2scmPcuAPGzbuhsLRFGx9BztEtaCLuFB9BaYyqVW+U9VqhsLBBm5ZE7tk/UPueLLtEl6It18JYdzGYuIx711q72jSr8oBrTa3hm7N3+d0vquBaa1GDfg2qALDtRnj+tZaQDkB9Z2smt6lFI9cn61qTeqTPuHEHjJt1LSiPY788vDye7o2y3tMF5XFuj648FA7uqNo8j5FLdYxsHMk5uoW8y4fKKDclo8boLnhP6IOZix2pt8O59tF6Eh7QXnNsU49Gc0ZgXdeDrOgk7ny9i6BC7TX3Xi2p81ZfLGu4olApSQ+M4s6qPwjbelwXpu57L1LvvRf1zpsVk8S+xhNKPoOlwGrQC9iOHITSyZGcwCASP19B9qXrBsOad34W64HPY1K3FgqVitzAYJJWryfr1Hm9cAorS+wnjcG807MobazJi4gk4cvVZJ04WxZZ+ke877XxzV3sSPYL59JD2vjOberRdM5wbO+18W+t2E1AoToDULV3SxpNG4RVdRfSgmO4tngL4XsKyqjWyOfwHtUFy3vtjuTbYdxY8htRh/LbbwpjJT7vD8L9uaZYVXcmNyWT6L+uc+Xjn8mKTir5QighH82awrixw7C3t+Xs2UtMfmsmvr5+xYb/88AvdOhQ9N7ojz/+5IV+IwFo92wr3n33DZo95UOVKm4MGDiGnTv3FYlTWUye+hovjeyPra01Vy7eYO77n3DndmCx4fsP6cMny+YUOd6oaltysnOKHB//1mje/XASa1f/xMIPvyzJpAtRIVS4ToHAwEDatGlDnTp12LRpEzVr1uTGjRtMnTqVPXv2cPr0aRwcin/oUBwTExPc3NxKIcWwZs0aevToQW5uLleuXOGVV17B0tKS+fPnl8r3PQmqvtCapvNGcHH6GuLP+eE1ojPPbpzGvg7TyAyPLxLeopozz/44lbsbD3N20gocW9ah2aJXyI5PIfz3/IexSnNT0oNjCNt1hiZzhxv8Xuc29QhYc5DEywEojJU0+uAl2v38AfvbT0OdmV2qeS4pbV/vQ+txvdj53iriA6NoN7kfwzZOZ0Wn98hJzzIYx9jclMSQGG7+foauHxkum7+5N/biqaGdiPYNLo3klyiXvm2oPX80tz/4juSzt/EY2YUmm2Zwpt07ZBuoR2aezjT5aToRP/6J78Rl2D5dl7qLx5Ebn0Ls72cA8PpgCG4D23Hr3dWk3wnHsWMTfNZM5UKfD0m7HgSAkYUpaTeCiNx0GJ8175Vllv8xt75tqD9/FL4ffE/i2dtUG9mF5ps+4Hi7d8kyUEbmns40/+l9wn48xNWJy7F/ui4NFo8lJz6F6N/zGx4KlZKWW2aSHZfM5bFLyIpMwKyKI+q0TN15siLjub1gExl3owHwGNyeZuve42SXD0i7HVY2mX9MrV/vw9PjerL7vdUkBEbxzOS+DNn4Ad90mlrstaYyNyUpJJZbv5+lSzHXWmpkAkc+2UxiUH7ZNBrYjoHfTuGHXjMf2OFQkey7FcFnh3yZ0bURTT3s2XolhIlbz7JtTAfcbcwNxpm26xLx6dnM7t6YavYWJGTkoNZo9MJYmRizfWwHvWOVtUNgn18Unx27zfSO9WhaxY5fr4czaeclfh3eBnfrYspoz1USMnKY/VwDPO3yyyiv0G5a58MT6VHHjSbudpgojVh3MYg3tl/k1+FtcLEyK6uslYvMzCzqenvRr1c33pm5oLyTU66UdVqg6vASOYd+QhMRgHHj9pj2m0zWhjloUxMNxjHp9SoKCxtyDqxHmxyLwtwajJ6MCcP7/KP57C8/pneoS1N3O369Ec6kXVf4dWhr3K0NXxfT9l7Lv9Y618fT1pyEzBzyNAauNTdbTIyNWHcxmDd2XObXoa2emGtN6pE+Ze3mqNoPIufwpvzy8GmHad9JZP04t/jy6PkqCgtrcg5uQJsUi8JCvzwUKhO0yXHk+l/EpP2gsspKianStzU+80Zy5YMfSDjnR40Rz9Hmp/c51H6q4faapzOtN04j+MfDXJj0NQ4t69Bk8Riy41OIvNdey0lKw++r7aTeiUCTk4db12Y89dV4suNSiC00OCLlVignBy3Uvdfed79QUVl064jDe2+QsGgpWVduYP1ib1yWLSJi4FjUUTFFwps18yHrzAWSlv+AJjUNq77dcflqPpEjJ5N7+16HlLExris/RZ2QRNy0eeTFxGLs6oImPaOMc/dw1Qq18WPP+eE9ojPtN05jb4dpZBioM5bVnGn/41QCNx7m9KQVOBdq44fdqzOOzb1ps2oy1z/dStiec1Tt2ZK2qyfzZ995JFwKACAzMoGrH/9M6r1765ovtePZNVPY33UGKX7hGJubYO9TA98lv5HkG4KJrSVPzRtBu3XvcqDHrLIroH9g6nsTePut1xgz7h38/QOZMf0t9v6xiQaN2pOWlm4wzsCXXsXERKV77+hoz8XzB9j6627dMUtLC65e9WXtus1s3fJdqeejNL06eRSvvDGUDybP5W5ACBOmjGXN1q/p0fpF0h9wfaSmpNG9jX7Ho6EOAZ+mDXhpRH9uXS++I0aUvSd1xH55qXB3cRMnTsTExIT9+/fToUMHPD096dmzJwcPHiQ8PJyZM2cCUKNGDRYuXMiYMWOwtrbG09OTb775ptjz3r8Ez9q1a7Gzs2Pfvn3Ur18fKysrevToQWSk/ojxNWvWUL9+fczMzKhXrx4rVqwocm47Ozvc3NyoVq0affr04YUXXuDixYu6zwMCAujbty+urq5YWVnRsmVLDh7UH3WbnZ3NtGnTqFatGqamptSuXZvvv//eYF4yMzPp3bs3rVu3JiEhgdGjR9OvXz+9MG+//TYdO3bUve/YsSOTJk1i0qRJ2NnZ4ejoyIcffoi2nLburjO+J3c3HSHopyOk+kdw5aMfyYiIp9aoLgbD1xr5HBnh8Vz56EdS/SMI+ukId38+Sp3Xe+vCJF4J5Nr8TYTtOI0mJ8/geY4P/ZTgLcdI8Qsn2TeEc++sxrKqE/ZNapZKPkvD02N7cHz5dm7tPU+sXxg73l2FysyERn2LHzEZeTWQPxdu4sau06izDZcNgMrClP7/m8Dv739HZrLhm42KpNrrfYj46RCRGw+R4R+O/6x1ZIfH4TG6m8HwHiO7kRUWh/+sdWT4hxO58RCRmw7jOeF5XRi3Qe0I+t9vxP95iazgGMLXHSDhyBU83ygIk3DoMoGLNxP7R8UbnXO/Gq/3Juynw4RtPEy6fwS3Zq0nKzwez9FdDYavNrIrWWHx3Jq1nnT/CMI2HiZs02FqTuijC1P15U6o7K24NPoLks75kRUWR9LZ26T6hujCxO6/SNyfl8kIjCQjMBL/RZvJS8/CtnntUs9zSWk5tgcnl+/Ab+954vzC2P3ualRmJjR4yLV2eOEmbu46TV52rsEwd/68RMDhKyTcjSLhbhTHPvuFnIwsqjTzLq2slLgN5+/S36caAxp74uVozbTODXGzNuOXy4Y7E0/cjeF8aDzLX2xJ6xpOeNha4ONuR1OP+zr5FeBkZab3qqx+vBRMv4YeDGhUFS8HK6a2r4ublRm/XDXcKXYiKI4L4Yks6/sUrT0dqWJjTiM3W5q62+nCLOzuw0uNq1HX2ZqaDpbM6twArVbLmdCEMspV+WnXpiVvvjaKrh2fKe+klDvjZl3Iu3EC9Y0TaBOjyD26BW1aIsaNOxgMb1S9IcqqdcjevgxN6C20KfFoooPQRBY/kq4y+fFyCP0aVGFAQw+8HCyZ2q4Oblam/HKtmGstOJ4L4Ukse74pras55F9rrvdda90a8ZJP1fxrzd6SWZ3q519rYYYfDldGUo/0FSmPY7/kl4dPceXRAGXV2mTvWJ5fHqlFy0MTHUzu8W2o/c6jVRd//11ReY/vRfCmI4T8dIQ0/wiuf7SBzPB4ahTTXqsx8jkyw+K5/tEG0vwjCPnpCMGbjuD9RsE9ZPzJm0TuOU+afwQZwTEEfreXFN8QHJ+uq3cubZ6a7Nhk3SsnPrVU81pSbIa9SNr2vaRt30Pe3RASP1+JOjoG64HPGwyf+PlKUtZtIcf3Nnmh4SQt/4HckHAs2rfWhbHq2wMjG2ti3/2I7Cs3UEfGkH35Orn+Fe/aq3uvjR94r41/6aMfyXyENv6le238wHtt/LqF2vh1Xu1J9LHr3Fy2k9Q7kdxctpPo4zeo82oPXZiIA5eIPHSFtMAo0gKjuLb4F/LSs3Bsnn9vnZuaydEhiwnddYbUgEjiL97h4sx1ODTxwsLj361EUdrenDyORYuXsn37Hm7cuM0rY97GwsKcl4f0LzZOYmIS0dGxuleX59qTkZHJ1l936cLs3XeYj2Z/yvbte8oiG6Vq1PiXWblkDft/P4z/rQCmTZqNubkZfV7s8cB4Wq2WuJh4vdf9LCzN+XzVfGZN+Zjk5Mrx+yPEv1GhOgUSEhLYt28fEyZMwNxcfxSdm5sbw4YNY/PmzboH2V988QUtWrTg0qVLTJgwgTfeeINbt4pfEuN+GRkZfP7552zYsIFjx44REhLCe+8VjPr99ttvmTlzJh9//DE3b95k4cKFzJo1i3Xr1hV7Tj8/Pw4fPkyrVq10x9LS0ujVqxcHDx7k0qVLdO/eneeff56QkIIHaCNHjuTnn39m6dKl3Lx5k1WrVmFlZVXk/MnJyXTr1o2cnBz+/PPPfzRrYt26dRgbG3PmzBmWLl3KkiVL+O67su8dVqiU2DWuSfTRa3rHo49ew7GF4QeGDi1qFw1/5Cr2TWqieIxRpCprCwByEtP+9TnKkl01Z6xd7An8q6As1Dl5BJ+5RdUSeNjac/5o/A9d5u6JG499rtKmUCmxbuxFwpErescTjl7FtkVdg3FsW9Qm4aj+8iTxhy9j3cRLV4+MTFRo7hspoMnKwfZpw+esyBQqJTaNaxJ337I0cUevYteijsE4di1qE3dfGcUdvopNoTJy6d6cpPN+NFg8hk7XV/HM0c/weqsfGBXTa2+kwK1fG4wtTEk6XzlGWthVc8bKxY67911rISV0rf1NYaSg/vOtUZmbEn7Rv8TOW5py1RpuRiXTpoaz3vHWNZy5Em74gdmRO9E0dLVl7dlAuq48yAvfHeHLw75k5ar1wmXmqOm5+hDdVv7J5F/PcSs6udTyUZpy1RpuxqTSxlO/odna04ErkUkG4xy9G0sDVxvWXgii2/fH6Lv+BF/+5UdWntpgeICsPDV5Gi22Zqpiw4gnjJESIxdPNMG+eofVwb4YudcyGEXp1RhNdDCqFt0xG7cYs1HzULV7EZSVv97orrVq+vfDras5cCXK8O/H0buxNHCxZu3FYLqtOU7fDSf58rj/o11rppW/zACpR/f7uzxCbuodVgffxMjdy2AUpVeTe+XRDbOxizEbORfVs09IeZB/D2nbuKbe6H2AmKPXcGhp+B7SvnltYu5rr8UeuYrdA9prTs82xMrbnfjT+mVv6eVG98tf0+XsVzRfNRkLT5fHyE0ZMTbGpH4dMk/rL/2TeeoCpk0aPNo5FAqMLCzQpBQ8hLTo0Ibsa744fPAmVQ/8gvuWb7EZ83KFm6VjpFJi37gmUffVgaij13Aqpo3v2KJ20fBHruJQqM44tvAm6r62SdSRqzgVUw8VRgqq9W2NsYUp8ReKX/5LZWOOVqMhJ7nizbioWdMTd3dXDhw8qjuWk5PDsb9O06ZNi0c+zyuvDGHzlh1kZGQ+PHAlU626By6uThw/clp3LDcnl7MnL9Ls6cYPjGthac7hi7s4duV3Vm9cQn2fou382Z+8z5EDJzh5rOIPAhTicVSo5YP8/f3RarXUr1/f4Of169cnMTGR2NhYAHr16sWECflrC77//vssWbKEI0eOUK9evUf6vtzcXFatWkWtWvk3v5MmTWLevHm6z+fPn88XX3zBgAEDAKhZsya+vr6sXr2aUaNG6cK9/PLLKJVK8vLyyM7Opk+fPkyfPl33eZMmTWjSpInu/YIFC/jtt9/YuXMnkyZNws/Pjy1btnDgwAG6dMnvRffyKnoDGh0dzeDBg6lVqxabNm3CxMTkkfL5t2rVqrFkyRIUCgV169bl2rVrLFmyhFdfffUfnedxmTpYY2SsJDtWv7GWHZuMmbPhtVrNnG0NhjdSGWPqYE1WTNK/SkuTOcOIO3OLlEqynImVix0AafeVRXpcMrYeTo917obPt8a9UU2+e6FiTqG8n8rBBiNjJTn3lUVObDIm98rpfiYudgbDG6mMUTlYkxOTRPyRK1Qb34ekUzfJDIrGvl0jnLq3QKGsWDfej8LkAWVkWkwZmT6gjEwcrMmOScK8ugsOzzYkctsJLgz9BAsvNxosGoNCaUTAl9t08azqV6P17/MxMlWhTs/i4itfkO5XOZbHsbxXPumlcK0BONetysjf5mBsqiInPYtt478i3j/isc9bFhIzc1BrtThY6v8NcrQ0JS7d8DJs4UmZXApPxMRYyZf9WpCUmcPCA9dJzsplbs/8v481HayY17Mx3s42pOfk8dOFu4z+6SSbR7enur1lqeerJOnKyOK+MrIwJT6j6GgkgPDkTC5HJGGqNOLL3k1IzMph0eFbpGTnMqdLQ4Nxlp7wx8XKlFbV/vmyiqJyUphboTBSos1I0TuuzUhFYWFjMI6RrTNGVbwhL5ecXavA3AqTzi+jMLMk58D6skh2qUnMzH3AtWZ4Bk14SiaXI5Pzr7VePiRm5rLo6O38a+05ww/ulp66c+9aK36fgspE6pG+YssjMwWFZTHlYeOUXx7qXHJ23yuPTi+jMLMg5+CGskh2qfq7vZb1T9prLnbExOo/vM267x4SwNjanO6Xv8bIxBitWsPV6WuIPVaw5n7ixTtcnLyStIAozJxtqfNOP9rtnsOhDtPIrcADuZR2tiiMlWji9QdIqBMSUTo+2t9pmxGDUJibkb6/4GGwsYc7Zi2fIn3Pn8S8OQPjah44fPAmCqWS5G8rzt5vJsXUmawH1BlzZ1uiDIQv3MY3c7YjKzblvjApRc5pW68az+2eg9JURV56FifGLCGlmHaHkamKxjOHEPzbSfLSKt4DczfX/E6w6Og4vePR0bFU96z6SOdo2aIpPo3q89prFXup23/LySV/4E38faP842PjqVLNvdh4gf5BfDB5Ln4372BlbcnI117m593f80KnlwkODAWgd79uNPCpx4vdRpZeBsS/Vk6LnTyxKtVTrr9nCPy9uW/jxgU9gAqFAjc3N2Jiiq7VVxwLCwtdhwCAu7u7Ln5sbCyhoaGMHTsWKysr3WvBggUEBATonWfJkiVcvnyZK1eusHv3bvz8/BgxYoTu8/T0dKZNm0aDBg2ws7PDysqKW7du6WYKXL58GaVSSYcOhqen/q1Lly54eXmxZcuWf9whANC6dWu9jZHbtGmDv78/arXhkVHZ2dmkpKTovXK1xY+i+qeKLF2kUPCg67vIxX8vL/92CaSmC0dj28CTM298/a/il4VG/dryvu/3updRcbMiFIrHWgrKxt2BbrNHsv3tFaiLWfKk4rq/HvHgvxT3faa7Ju4d9/9wDZl3o2h94is6hv1EnUVjifz5CFp15VjL1LCiZfTA+lLk2vz78L3fYCMjcuJSuP7uN6RcvUvU9lMEfvUb1e5bkij9TgQnO7/P6V6zCF13gMZLJ2BZx+NxM1MqGvZry7u+3+lef19rRX92FEUP/gvxgZH80HMm6/rN4eKPf9Lni/E41q7y+CcuQwr0Z4ZotVoUxUwW0dz7bGGfpvi429HOy4X3OtVn5/Uw3WyBxlXs6d2wKnVdbGhW1YFPX2iGp70VP18MKuWclJ77i0PLQ8oI+Lh7Ixq52dKuhjPvtqvDTt8IgyOY114IYq9fFJ/3blJp910QZUShALRk7/0+f3mToOvkHt2KskGbJ2dU8/2/R2iLXH9/02jzr82PuzWikast7Wo48e6ztdl5M9LwtXYxmL1+0Xze0+e/fa39B+qRgcZG8X/zdeXxQ0F5HPvlySoPKHoLqXjIA5mH3GcD5KVlceS56RztMYubi7fQaM5wHNsWDAqMOXSFyN/PkXorlNi/rnN6+GcAeL7U/rGyUla0/7jQ8ll074Tt+BHEfbAATWJSwQdGRqgTkohfsIScm/5k7D9C8vcbsSpmSaJyZ6CN/+Dw9wcvWmeK1quix1IDItjfZQYH+8zmzvo/eXrp69gYaHcojJW0WTUJhZGCCx+sfXDaysjLL/cnKcFP91Kp8sfu3t9eU/yDNv8rr7zMtes3OXf+ckknt1w8/2IPLgUd072M/y4j/lkZXblwnZ1b93Drhj/nT1/mrbEfEBQYzIhxgwFwq+LKzI/fZeqEWQb3GRDiSVOhZgp4e3ujUCjw9fUtskY+wK1bt7C3t8fJKX+Upkqlf8OlUCjQ/INNiAzF//sH5O/zfPvtt3pLAQEolfoNAjc3N7y989erq1u3Lqmpqbz88sssWLAAb29vpk6dyr59+/j888/x9vbG3NycgQMHkpOT/yNz/1JJxenduze//vorvr6++Pj46I4bGRkV+eHLzX38B7uLFi1i7ty5escGWTbiJesHT8d6mOyEVDR5aszuG6ls6mRTZDbA37JikzFzsS0SXpOb96+W/mm6YCRVujXjSP/5ZEZW3LWY/Q5cJPxSQSeUsUn+JWvlbEtaodkRlo42pMf9+2U23H1qYuVsy7jdBZs3Ghkrqd6qHi1HdWNh7VFoNSXwJLQE5SakoMlTY+Jsp3fcxMm2yEj3v+XEJBWZRaC6V4/+HnmUG5/KtdGfYWSqwtjeipyoRGp9OIzMkEfvcKwocv5FGWUbKCMTJ1u9MsqOTkSTp85/snJPmn8EZq72KFRKtPce8mpz1WTc2/Ar5UogNk1rUePVntyYWvE2tfI/cJGIQteastC1ll7oWrN4zGvtb5pcNYnB+WUTde0u7k28aPlKD/bO+OGxz13a7M1NUCoUxN83KyAhIwdHC1ODcZysTHGxMsO60NIbNR2t0ALRaVkGZwIYKRQ0dLclJLHi729yP10ZZeg3JhIycnAwN9yp72RpiouVqX4ZOVgWlJFdQRmtvxjE9+fusqp/M+o4WZdKHkTFpM1MQ6tRFxnNrbCwLjLKWRcnPRltWhLkFGyQrkmIRKEwQmFtjzap8v19+5u9ueretVb09+j+2QN/c7IwuXetFTSDatr/fa1lU93OQnd8/cVgvj8fxKq+Tz1R15rUI3268rDUb2sozP9peUQ9EeUBhdtr+mVi4mRLdjH3QVkxSUVmohpsr2m1pP99f3gjGOvaHtSZ3JdTJ/WXEPqbOiOblJuhWHq5/fsMlQF1UjLaPHWRWQFKezvUCQ/ej8SiW0ccP3qXuPfnk3X2ot5n6rh4tHlqKPScI/duCMbOjmBsDHkVY7+KnGLa+GZONkVmD/wt8wFt/Ox7dSYrNslgmKw4/WtTk6sm7V69SrxyF4cmXtQZ153z0wrurRXGStp+Mxmras4cHrSwwswS2LVrP2fPXtK9NzXN//vl5uZMVKENql1cnIiOiSsS/37m5mYMfukF5sz9vOQTW04O7T3GlYsFM4r+HiTr5OJEbHTBbAEHJwfiYx/9+Y5Wq+XaJV9qeFUDoFGTeji5OLKt0IwvY2NjWrZ5iuFjX6KRR9t/9MxRlDzZaLhkVaiZAo6OjnTt2pUVK1aQman/Ax0VFcXGjRsZPHiw3mj30uLq6oqHhweBgYF4e3vrvWrWfPCmtH93Gvydh7/++ovRo0fTv39/fHx8cHNzIygoSBfex8cHjUbD0aNHDZ1OZ/HixYwaNYrnnnsOX9+CNUCdnZ2LbJB8+fLlIvFPnz5d5H3t2rWLdHL8bfr06SQnJ+u9+lsZXsLgn9Dmqkm6ehfX9o30jru29yH+vOE1tRPO++Pa3kc/fAcfEq/czb9J+geafjwKj14tOTboYzJCY/9Z4stYTnoWicHRulesfzipMYnUfLZQp5Aq/+F92IV/vx753RM3WNX1fb7pOUP3irgSwLXtJ/mm54wK1yEA+fUo9WogDh30O6kc2jcm+fxtg3GSz/vj0P6+8B2bkHolsEg90mTnkhOViMJYiXOfVsTt018ftDLQ5qpJuXoXpw76145Te59i1/ZPOu+P033XmlPHxqQUKqPEc35Y1nDTG/ljWcudrKgEXYeAIQqFAiOTijl67v5rLc4/nLSYJGo8W/A7ZaRS4vmY11pxFAqFriOiolMpjajvZsupYP3fzzPBcTTxMLy0RlMPB2LTssgotAl8cEI6RgpwLWYzYa1Wy+2YFJwsDXc0VGQqpRH1Xaw5HaI/pfl0SAJNCm1mWljTKnbEpmfrl1FiRpEyWnchiG/P3uXrvk/R0NXwdHzxBNOo0cSEYOSpv9Sm0rM+msgAg1HUEQEoLO1AVXAtGdm7otVo0KZW7o1zddfafZttnw5NoImb4eujqbuBay3p72utoIzWXQzm2/N3+fqFpjR0NbyETKUl9UjfA8vD8Gau6sgnuDzIv4dMvnoX5/vuIV06NCLhnOF7yMQL/rh00G/fOXdsTNLD2msKMDIt/h7IyMQY69pVyIqu4OWal0fOTT/MWzXXO2zWujnZV3yLiZQ/Q8BxzlTiZi4k8/iZIp9nX7mBqloVvftuVfWq5MXGVZgOAbg34OXqXdwMtPHjimnjxxto47t18CGhUJ2JP3/HQJjGxBVTD/+mUKDX7vi7Q8C6phtHBi+qUHsKpqWlExAQpHv5+voRGRlNl+cKZseoVCrat2vNqVMPb5MOGvgCpqYmbPxp20PDVhbp6RmE3A3Tve7cDiQmOo5nOhQM4FWpjHm6bTMunr36gDMVVb9RHWLudSycOnaO3u0G07fTMN3r2qUb7Nq6l76dhkmHgHjiVKhOAYDly5eTnZ1N9+7dOXbsGKGhoezdu5euXbvi4eHBxx9/XGZpmTNnDosWLeJ///sffn5+XLt2jTVr1vDll1/qhUtKSiIqKoqIiAiOHj3KvHnzqFOnjm5vBG9vb7Zt26ZbYmjo0KF6PyY1atRg1KhRjBkzhu3bt3P37l2OHDnCli1biqTp888/Z9iwYXTu3Fm3qXLnzp05f/4869evx9/fn9mzZ3P9+vUicUNDQ5kyZQq3b99m06ZNLFu2jLfeeqvY/JuammJjY6P3UilKZtq03+o91BzaiRpDOmBduwpN5g7HwsORwPV/AtBoxmBaLn1dFz5g/Z9YVHWk8ZxhWNeuQo0hHaj5ckf8Vv2uC6NQKbFtWB3bhtUxUhlj7maPbcPqWNZw1YV5atFoPF98hjMTvyY3LQtTZ1tMnW0xqkSbNJ79fi/PTnyBut1b4FynKn2/eJ3crByu7zipC9P3y9fpPG2w7r2RSolrg+q4NqiO0sQYazd7XBtUx756ftnkpGcR6xem98rJyCYzMZVYv4q730Loqt1UGfYc7i93wqK2B97zRmFa1YmIdQcA8Jr5MvWXTdSFD1+/H7NqTnjPHYlFbQ/cX+5ElaGdCVmxSxfGppk3zr2exqy6C7at6tHk5xkojBSELN+hC6O0MMWqYXWsGlYHwNzTBauG1TH10N9UtCIIWvU7VYd1xuPljljWrkK9eSMxq+pEyLqDANSZOQSfZRN04UPXH8CsmhP15o7AsnYVPF7uSNWhnbi7YndBmLUHUDlYUf/jUVh4uePc5Sm83upLyJr9ujC1ZwzBvlU9zKs5Y1W/GrWnD8ahbQMifj1edpl/TOe+30vbiS9Qp3sLnOpUpc8X48nNysG30LXW58vxdJj2ku69kUqJSwNPXBp4ojQxxsrNAZcGnrprDaDD1Jeo2rIutlWdcK5blfZTB+HZuj43tp+kshjRoia/XQ1l+7VQAuNT+eyQL5EpmQxs4gnA0mO3+PD3y7rwvepXwdbchI/2XCEgLpULofEsOXqLvj7VMFPl/11ZdcKPk3djCUvK4FZ0MnP2XsUvJoWBTauXRxYf2/CnqvPbjXC23wgnMCGNz4/dJioti4E++WvBLj3hz4f7C/5W96zjhq2ZitkHbxAQn8aF8ES+OuFP3wYemN1bsmTthSC+PnWH2V0aUMXGnLj0bOLue7j5pMrIyOSWXwC3/PIfWIZHRHPLL4DIqMo9GvffyLt4EONGz6Js0BaFvRuq9oNQWDuQd/UYAKpn+mHSbbQuvPr2WbRZaZh0HYXCwR0jj9qo2r2I+sYJUFe2JQOLGt7Uk998I9juG0FgQjqf/+VHVFo2AxvlLxux9OQdPjxwQxe+Zx3X/Gvtz5sEJBS61upXKbjWLgbz9ekAZnduQBVrsyfyWpN6pC/v4kGMGz5zX3nYk3ftXnm0vb88zt0rj5H55VHFG9WzA1D7niwoDyMlCqeqKJyq5v/byi7/va1zOeTwn7uz+g+qD+2E58sdsKpdhUZzh2Pu4UTQvfZa/RmDabbsDV34oPV/Yl7ViYZzhmNVuwqeL3eg+ssdubOy4B6y9uQXcG7fCAtPF6y8q1BrfC+qDWpH6NaC+8OGs4fi2KYeFp7O2D9Vi5bfvY2xtTmhW/4qu8z/Sykbf8Wqf08s+/bAuKYn9u++gbGbC6m/5rc17CaNxXHe+7rwFt074TTvfRKXrCb72k2MHO0xcrRHYVUwOzD1l10Y2dpgP3Uixp4emD/bCtsxQ0ndsrPM8/cwt++18Wvea+M3vdfGD7hXZ3xmDKbVfW18y6qONL3Xxq95r41/u1Ab3++7vbh18KHexD5Ye7tTb2IfXNs1xO/bvbowPtNfwqlVXSyqOmFbrxo+HwzCuW0DgredAEChNOKZb9/CobEXpyeuQGFkhJmzLWbOthipKuaycEuXfccH70+mb98eNGxYlx++X0JGRiabfv5NF2bND//j4wUfFIk75pUh7Ni5jwQDM1QsLS1o0qQhTZrkD/asWcOTJk0aUq1a5VrGFGDd6k28/vYrdO3Vkdr1arF42RwyM7PY/WtB3fh0+Vze/bDgecCk917l2U6tqVbdg/qN6rDwfx9Rr1Fdfl77K5Df+eB/K0DvlZGRRWJiEv63DHeai7Kl1SrK7fUkqnDDEmvXrs358+eZM2cOgwcPJj4+Hjc3N/r168fs2bNxcCi7zfTGjRuHhYUFn332GdOmTcPS0hIfHx/efvttvXCvvPIKULCvQfv27Vm4cCHGxvnFu2TJEsaMGUPbtm1xcnLi/fffJyVFf7rbypUrmTFjBhMmTCA+Ph5PT09mzJhhMF1LlixBrVbTuXNnjhw5Qvfu3Zk1axbTpk0jKyuLMWPGMHLkSK5du6YXb+TIkWRmZvL000+jVCqZPHkyr732WgmV1j8TtvM0JvZW1J/SHzMXO1Juh3F8+GdkhOVPhzNzscOi0APWjNBYjg//jCZzh1NrdFeyohO5PGs94b+f04Uxd7Wn68GFuvd1J/Sh7oQ+xJ705eiL+Z1Jte6ted5xm/5muufeWk3wlmOllt+SdHLVbozNTOi5YDTmNpaEXw5g4/DF5KQXTF+2qeKoN7rf2tWe1/YUlE3b8X1oO74PQad82TCk7DraSlrMjlOo7K2pMeVFTF3tSbsVytWhi8i6V49MXewxK7QpbFZILFeGLqL2vFFUfaU72dGJ+M1cQ+zvBaNyjExVeH0wBLPqLqjTs4j/8xK+E5eTl5KhC2PdtBbNfpuje197Xv7G45E/H+HmWytKOdf/TNSOU6jsrfCe8iKmrnak3grlwtDFemVkXqiMMkNiuTD0E+rNG4nnK93Iik7k5sy1RP9+VhcmKyKe84MXUm/eSJ45/AnZUYkEf7uXwGUFHSemzrY0Xj4RU1c7clMzSPUN4fyQRcQf0/9dqshO37vWui8YjZmNBRGXA/h5+Cf3XWtORa61sYWutdbje9N6fG+CT93kp3vXmqWzDc8veR0rFzuyUzOIuRXK5pGfEnS8aGduRdW9XhWSMnNYfdKfuPRsvJ2sWP5iS6rY5i+7EZuWTWRqwYw/CxNjVg1qxeI/bzBsw3FszU3oVtedic/W1YVJzc5j/v5rxKVnY2VqTD0XG74f0gafYkbWV3Td67iRnJXLN2cD88vI0YplLzxFFZv8JQPjMrKJSi2oSxYmxqzs15xPjt5i+OYz2Jqp6FrblYltvHVhtlwNJVejZeof+iOgxj/txeuta/Eku37LnzGTCx6kfLrsGwD69uzCxx++W17JKhdqv/Pkmlmiat0bhYUt2vgIsncsR5uaP1peYWmLwqbQ/XJuNtnbvsKk4xDMXp6BNisNtd8Fck/uKOYbKpfutV3zr7VzdwuutT5NCl1rOUWvtb5P8ckxP4ZvOZd/rXm7MrG1ly7Mlmth+dfaXv2/WeNb1uT1Vl48CaQe6VP7XyDX3ApVq94oLGwMl4f1feXx2//yy2PI9Pzy8L9A7smCB7UKSzvMh32oe2/UvBuq5t1Qh/mR/av+ILOKKGJHfnut7pQBmLrYkXorjNPDPiXz7/aaqx3mhdtrIbGcHvYpjeaOoOYr+e21ax+uI7JQe01pYUrjxWMwd3dAnZVD2p0ILkxaQcSOghntZu6OtFg5OX9z4vgUEi/c4a/es3XfW5Fl7D9Cgq0Ndq8OR+nkQE5AEDFvzkAdmd+BrXRywNjNRRfe+sU+KFTGOE5/E8fpb+qOp+3cR/yc/L0U1NGxRE98H4d3J2C9+VvyYuJI2bSNlLWbyzZzjyB052lM7a1oeK+Nn3w7jL8KtfHN72vjp4fGcmz4Zzw1dzjeo7uSGZ3IpVnrCStUZ+LP+3Pq9eX4fDCIRtMGkR4czcnXl5FQaNlPMydbWi97AzOX/HZHkm8ox4Z+QvS9DazN3R3w6JE/g6P7n4v00nxowAJiTxleuqo8ffb5CszNzVi+dCH29racPXuJnr2HkpZWsKymZ7UqRUau167txbPPtqJHzyEGz9uieRP+PLhV9/6Lz+cAsG79FsaOe6fkM1KKvl22DjMzU2Z/+gG2ttZcuXidMYMmkZ5e0G53r+qGRltQRta21sz/YibOLo6kpqThe/02w154lauXbhj6CiGeeArt4+xOKiqNjh070rRpU7766qvHOs9W92Elk6An2M1/vgf0f84zWZV/RFlpy6l4E7kqnEtmFa5fu8J5e5bLwwP9h2llA7GHUg2eUt5JqPByf/ykvJNQ8RWzVKUoRP3PlsP8TyqDJWQruwOLDO+BIAo0rfLfm2H2T52OdH14oP+4YfFHyjsJFZ6XrXt5J6HC84utfEsUl7eARt3L7btrXd9Xbt9dWuSJihBCCCGEEEIIIYQQQogKSyvbOpQoGYoqhBBCCCGEEEIIIYQQQvxHyEyB/4gjR46UdxKEEEIIIYQQQgghhBDiH9M8oRv+lheZKSCEEEIIIYQQQgghhBBC/EdIp4AQQgghhBBCCCGEEEII8R8hywcJIYQQQgghhBBCCCGEqLC0snxQiZKZAkIIIYQQQgghhBBCCCHEf4R0CgghhBBCCCGEEEIIIYSosLQaRbm9/o0VK1ZQs2ZNzMzMaN68OX/99VexYbdt20bXrl1xdnbGxsaGNm3asG/fvn9bVI9EOgWEEEIIIYQQQgghhBBCiBKwefNm3n77bWbOnMmlS5do164dPXv2JCQkxGD4Y8eO0bVrV/744w8uXLhAp06deP7557l06VKppVH2FBBCCCGEEEIIIYQQQghRYWm15Z2CR/fll18yduxYxo0bB8BXX33Fvn37WLlyJYsWLSoS/quvvtJ7v3DhQnbs2MGuXbt46qmnSiWNMlNACCGEEEIIIYQQQgghhDAgOzublJQUvVd2drbBsDk5OVy4cIFu3brpHe/WrRsnT558pO/TaDSkpqbi4ODw2GkvjnQKCCGEEEIIIYQQQgghhBAGLFq0CFtbW72XoRH/AHFxcajValxdXfWOu7q6EhUV9Ujf98UXX5Cens5LL7302GkvjiwfJIQQQgghhBBCCCGEEKLC+rcb/paE6dOnM2XKFL1jpqamD4yjUOinV6vVFjlmyKZNm5gzZw47duzAxcXlnyf2EUmngPhHKtHyXeVGXd4JqASumZqUdxIqvHba1PJOQoXnmWtd3kmo+JTyZ/5BFNWrlncSKrzcHz8p7yRUeKrh75d3Eiq8vAt/lHcSKj6NprxTUPEZyST3h2nfbmt5J6HCSwtSlncSKjwfi6TyTkKFV09TrbyTUOG9buJd3kkQokSZmpo+tBPgb05OTiiVyiKzAmJiYorMHrjf5s2bGTt2LL/88gtdunT51+l9FHJnJYQQQgghhBBCCCGEEKLC0mgV5fb6J0xMTGjevDkHDhzQO37gwAHatm1bbLxNmzYxevRofvrpJ3r37v2vyuifkCGEQgghhBBCCCGEEEIIIUQJmDJlCiNGjKBFixa0adOGb775hpCQEF5//XUgfzmi8PBw1q9fD+R3CIwcOZL//e9/tG7dWjfLwNzcHFtb21JJo3QKCCGEEEIIIYQQQgghhBAlYPDgwcTHxzNv3jwiIyNp1KgRf/zxB9WrVwcgMjKSkJAQXfjVq1eTl5fHxIkTmThxou74qFGjWLt2bamkUToFhBBCCCGEEEIIIYQQQlRY2n+4jE95mzBhAhMmTDD42f0P+o8cOVL6CbqP7CkghBBCCCGEEEIIIYQQQvxHyEwBIYQQQgghhBBCCCGEEBWWVlveKXiyyEwBIYQQQgghhBBCCCGEEOI/QjoFhBBCCCGEEEIIIYQQQoj/CFk+SAghhBBCCCGEEEIIIUSFpalkGw1XdDJTQAghhBBCCCGEEEIIIYT4j5CZAkIIIYQQQgghhBBCCCEqLK3MFChRFX6mwJEjR1AoFCQlJZV3UiqUGjVq8NVXX5V3MoQQQgghhBBCCCGEEEJUIv96psDo0aNZt25d/kmMjXFwcKBx48a8/PLLjB49GiOjkulvaNu2LZGRkdja2pbI+YKCgqhZs6buvZ2dHT4+PsyfP58OHTo81rkVCgW//fYb/fr1e8xUFli7di1vv/12kU6Rc+fOYWlpWWLfUxZqjepC3Qm9MXOxI8UvnMsfbSDuzO1iwzu1qUfTOcOxqeNBZnQSt1fsJnD9n3phPHq3pNG0QVhWdyE9OIZri7cQsee87vMG7w6g4Xsv6sXJikliV5OJBefo1QKvEc9h37gmpg7W7O8yg+QbwSWU69LR8e0BNB/aGTNbS8Iv3eH3WWuJ9Q8vNrxzbQ86vTuQKo1qYlfNmb1zN3D6h71Fwlm72tN1+hC8OzZBZWZCfGAUO6Z9Q+T1oFLMzeNr9c4AGg3thJmtJVGXAjg8ay0JfsWXB4B3z5a0fm8gtp4uJIfEcOrTXwjYV1B3VJZmtHlvILW6t8DCyYaY60Ecm/Mj0VcDDZ6v86Ix+AzrzNG5G7j8/b4SzV9pcBrZE9fx/VG52JPlF0Lo3O9JP+trMKyxiz1VZ72ChY83pjXdif1hN2Fzvy8STmljSZVpw7Hr0RqlrRU5odGEzV9DyuELpZ2dEuHz7gC8h3XCxNaS+EsBnJuxluSH1KNqvVrSZNpArKq7kBYcw+XFvxC2t6AeubSqS/0JvXHwqYmFmz1HxywhbK9+efi8O4DqfVtjWcUBdY6ahGt3ubL4F+IvBZRKPkvK5ot3WXfmDnFpWdRysmZqFx+aVXMsNnxOnprVJ27zx40w4tKzcbU2Y1ybOvRrUl0XJiUrl+XHfDl0O5KUrFw87CyY0rkR7Wq5lkWWStzmEzdYd+QKcSkZ1HKzZ2rftjTzcjcYdtamw+w671fkuJerPdumvQTAjrO3mb35SJEwZxaPxVT15Ez+NG7cAePm3VBY2qKNjyDn6BY0EXeKj6A0RtWqN8p6rVBY2KBNSyL37B+ofU+WXaIrgPOXr7Hmp6343rpDbHwC/1s0i+faty3vZJWJzX9dZd2fl4hLSaeWmwNTX2xHs1oeBsPO+vEAu87eKnLcy82BbTOGATB26TYu3Cn6+/9sg+osf/2Fkk18Gdl8/BrrDl2693vkwNT+z9KsVhWDYWdt/JNd5wyVkT3bPhiqe5+Skc3yP05z6GogKRnZeDjYMKVfW9o1qFFa2ShVUo8ezrRbX0z7DsHIzhF12F0y1ywn79Y1g2GV9XywGPYaRh6eKEzN0MRGk31gJ9m/by0USIlZ/2GYdOiOkYMz6ogQMjd+Q97ls2WUo5JnNfAFrIe/hNLJkdzAIBK/XEHOZcNlZN7pWaxefAFVnVooVCpyA4NJ+XYdWafP65/z5QFYvfgCSlcXNMnJZP55jKSvv4Oc3LLIUomzH9Ybx1cHYOziQLZ/CNHzvyHj/A2DYY2d7XGdMQ6zRt6Y1KhCwrqdRC/49r5ASpxefwm7Ac9h7OZITmAY0Z+uJf1Y5WiDFGfCe+MYOKIvNrbWXLvoy4LpnxFw+26x4fsO7s3HS2cVOd7Msz052TkANG/dlFcmDqdB47q4uDnz5uhpHNpzrNTyUNKefmcADYd1wtTWkuhLARz98OHt/lo9W9LqvYHYVnchOTiG05/9QmCh9trIk0uwqeZcJN7VdQc49mH+M09zJxvazhiCZ3sfTGwsiDhzm2Oz1pEcFF2yGRQPpdWWdwqeLI/VguzRowdr1qxBrVYTHR3N3r17eeutt9i6dSs7d+7E2PjxG6gmJia4ubk99nnud/DgQRo2bEhMTAwzZsygV69eXL9+Xa/D4G+5ubmoVKoST8PjcHYu+qNVkVV9oTVN543g4vQ1xJ3zw2tEZ9ptnMbeDtPIDI8vEt6imjPtfpxK4MbDnJm0AqeWdWi26BWy41MI//0cAA7NvWm9ajI3Pt1K+J5zePRsSZvVkzncdx4JhR6mJd8K5ehLi3TvtRqN3ncpLcyIO+tH2K4ztPji1VIqgZLzzOt9aDOuF9vfW0V8YBTtJ/dj5MbpLOv0HjnpWQbjqMxNSQyJwff3M3T/aLjBMGY2Foz9dTZ3T/mycdSnpMenYF/dlayUjNLMzmNr/kYfnhrXkwPvriYpMIqWb/al/8YPWN9xKrnFlIdbM296fj2JU19sJWDveWr1aEHPFZP45cX5RF/OrztdPh2HY92q7Ht7JenRSdQb8Az9f/qADc+9T3p0ot75vLo1x61pLdKiEko9vyXB/vlnqTp7LKEzV5N+/iZOw7rjvf4jfDtPIjcirkh4IxMVefEpRC37BZdxhhuxCpUx3j/NJS8umcDXPyE3Mh5VFSc0aZmlnZ0S0WBiH+q/1pNTb68mJTCKRm/3pfPPH7Cr3VTyiqlHTs29eXbVJK5+upXQveep1qMF7VZPYn+/+boH+sYWpiTdCCHw52O0//5tg+dJDYzk/Mx1pAXHoDQzod5rPem86X12tn2X7ITU0sryY9l3M5zPDl5jRvcmNPVwYOvlICZuOcW2cZ1xt7UwGGfa9vPEZ2Qzu1dTqtlZkZCRjVpTcFeXq9bw+s8ncbA05bP+LXG1Nic6JRMLk8r5sHvfpTt8tuMkMwY8S9Oabmw95cvEb/9g27SXcLe3LhJ+Wr+2vNW7le69WqPhpS+20rWJl144KzMTtr8/WO/Yk9QhoKzTAlWHl8g59BOaiACMG7fHtN9ksjbMQZuaaDCOSa9XUVjYkHNgPdrkWBTm1lBCg1Mqk8zMLOp6e9GvVzfembmgvJNTZvZd9OOzbX8xY1BHmnq5s/XEdSau3MW2GcNwdzBwrb3YnrdeKOgsUau1vPTJJro29dYd+3JsL3LVat37pPQsBn+yia5P1S7dzJSSfRf9+ey348wY2CH/9+jkDSau3sW26UMN/x4NeJa3nm+te6/WaHnp05/p2qSgjHLz1Ly+cicO1uZ8NroHrnaWRCelYWFqUiZ5KmlSjx5O1bYT5q9MIuPbr8i7fQ3Tri9gNfNTkt8ZhTYupmiErEyy9v6GOjgQsrMwrueDxWtT0GZnkXNwNwDmQ8Zi0r4r6as+RxMegnHTllhNnU/qzImogx7QGVxBmXftiN2UCSR+spTsK9exGtAH5/8tIuqlMaiji5aR6VONyTpzgeQV36NJTcPy+R44fbmA6NGTyPXLz79Fj+ewm/gqCfM/I/vqDYw9q+I4exoASUtWlmn+SoJN73a4ffgqkbNXkHHhJvYv98Dzh7nc6f4GeZGxRcIrTFTkJSQTt2IzDq/0M3hOlykjse3bkciZy8gOCMOqXTOqrZxJ0KD3yPI1PKCrohszaQQjX3+ZD9+cT1BgCOPfeYVvtyylT9vBZKQX3z5PTUmjT9uX9I793SEAYG5hzu0b/mzftJuv1iwutfSXhmZv9KHpqz05OGU1SXejaPFmX/r+9AE/dnhwu7/7ikmc+byg3d99xSS2DSho92/p8xFGyoL7Roe6Vem3aToBuws6J3t/9w7qPDW/j11CTmomTV/tSd9N0/mp8/vkZWaXbsaFKEWP1WIyNTXFzc0NDw8PmjVrxowZM9ixYwd79uxh7dq1ACQnJ/Paa6/h4uKCjY0NnTt35sqVKwDcvn0bhULBrVv6oyy+/PJLatSogVarNbh80IkTJ+jQoQMWFhbY29vTvXt3EhPzG4parZZPP/0ULy8vzM3NadKkCVu3buV+jo6OuLm50bhxY1avXk1GRgb79+8H8kf8r1q1ir59+2JpacmCBfkNq5UrV1KrVi1MTEyoW7cuGzZs0J2vRo0aAPTv3x+FQqF7D7Br1y6aN2+OmZkZXl5ezJ07l7y8PN3nSUlJvPbaa7i6umJmZkajRo3YvXs3R44c4ZVXXiE5ORmFQoFCoWDOnDm67yu8fFBISAh9+/bFysoKGxsbXnrpJaKjC3ot58yZQ9OmTdmwYQM1atTA1taWIUOGkJpaNg+c6ozvyd1NR7j70xFS/SO48tGPZETEU2tUF4Pha418jozweK589COp/hHc/ekId38+St3Xexec89WeRB+7zq1lO0m9E8mtZTuJOX6D2q/20DuXNk9Ddmyy7pUTr5/nkK3HubnkN6KPXS/5jJeC1mN7cGz5dm7uPU+MXxi/vbsKlZkJPn2LHw0YcTWQAws3cX3XadTZeQbDPPvG8yRHxrNj6jeEXwkkKSyOuydukBhi4Ca/AnlqbA/OLd9BwN7zxPuFcWDKalRmJtTtV3x5PDW2ByF/Xef817tIDIjk/Ne7CD3hy1Nj8+uO0lSFd8+WHF/4MxFnb5McHM2ZJdtICY2l8Yjn9M5l6WpPx/mj2PvWCjS5akNfV+G4vNqX+M0Hif/5AFl3wgib+z25EXE4j+hpMHxOWAxhc74j4dfDqFPTDYZxHNwFYzsrAsYtJP38LXLCY0k/d5PMm0GlmJOSU29cD64v3UHonvMk3w7j1FurMTY3oUb/4utRvVd7EHXsOjeW7yLlTiQ3lu8i6rgv9Qr9BkUcvsqVT7cSuud8secJ+u0UUX/dIC0klmS/cC7M2YiJjQV2DTxLNI8lacPZO/RvUp0BTarj5WTNtC4+uNmY88ulIIPhTwRGcz40juWDWtO6hgsedhb4VLGnaVUHXZjtV4NJycphyYCneaqqI1VsLXiqmiN1XUtmpmBZ23DsGv2frseA1vXxcrVnWr9ncLOz4peThmfkWJub4mRjoXvdCI0lJTObvi3rFglbOJyTjeFOmMrKuFkX8m6cQH3jBNrEKHKPbkGblohxY8OzOY2qN0RZtQ7Z25ehCb2FNiUeTXQQmsjK+RDgcbRr05I3XxtF147PlHdSytSGw5fp37oBA9o2xMvNgWkvtsfN3opfjhsemZt/rVnqXjdCo0nJzKJv6/q6MLaWZnphTt8KxUxlTLdCD3wrkw1HLtO/VX0GtGmQX0YD2uFmZ80vxw3f+xYpo5CY/N+jVvV0YbafuUlKRhZLxvbkKS93qjjY8JRXFep6OJVVtkqU1KOHM+sziJxDf5Bz6Hc04SFkrl2OJi4G0259DYZXB90h98QhNGFBaGKjyPnrALlXzmFcv7EujEn7bmRt20jepTNoYiLJ2b+T3MvnMH1+sMFzVnTWQweSvmMP6Tv+IC8ohKQvV6COjsFq4PMGwyd9uYLUDZvJ8b1NXmg4ySu+Jy80HPP2bXRhTHwakH31Ohn7DqGOjCb7zAUy9h/GpH7R+4PKwHFMfxJ/2U/Slv3kBIQSveBbciPjcBjWy2D43PAYoud/Q/Jvh9AU0w6x7deJuJVbSDtyntzQKBJ/+oO0vy7iMHZAaWalVI14bTDffLWWg38c4c6tQGZMnoeZuRm9B3R7YDytVkt8bILeq7Djh06xbPFqDv5xpBRTXzqajO3B+WU7CNx7noTbYRx8ZzXGZibUeUC7v8m4HoT+dZ0LX+8iKSCSC1/vIuyEL03GFbTXshJSyYhN1r1qPPcUSUHRhJ++CYBdTTfcmtfm6Iw1xFwJJCkwkqMz12BiaUqdvm2K+2ohKoUSH0bVuXNnmjRpwrZt29BqtfTu3ZuoqCj++OMPLly4QLNmzXjuuedISEigbt26NG/enI0bN+qd46effmLo0KEoFEU3kLh8+TLPPfccDRs25NSpUxw/fpznn38e9b1RGB9++CFr1qxh5cqV3Lhxg3feeYfhw4dz9OjRYtNsYZHfoM7NLZh+N3v2bPr27cu1a9cYM2YMv/32G2+99Rbvvvsu169fZ/z48bzyyiscPnwYyF/OB2DNmjVERkbq3u/bt4/hw4fz5ptv4uvry+rVq1m7di0ff/wxABqNhp49e3Ly5El+/PFHfH19Wbx4MUqlkrZt2/LVV19hY2NDZGQkkZGRvPfee0XSr9Vq6devHwkJCRw9epQDBw4QEBDA4MH6N1MBAQFs376d3bt3s3v3bo4ePcrixaXfO6xQKbFvXJOoo/o31dFHr+HUwvBIGccWtYm+L3zUkavYN6mJwlh5L4w30UevFgnj2LKO3jErL1f6XFpOrzNLaLVyEpaelWuWRWH21ZyxdrEn4K+CslHn5BF05hbVmj/eqKO6XZsTcfUug1a8ydQLKxj/x8c0G9LpcZNcqmw8nbF0sSPkmH55hJ25hfsDysO9mbdeHICQo1d1cYyMlRgZK1Fn60/JzcvKoUrhB3QKBd2/ep2Lq39/6LTFikKhMsbCpxYpxy7rHU85dhnLFvUMR3oEtl1bkn7hNp4LxuNzcR31Dy7FddLASjFa18rTGXNXOyIL/eZocvKIPn0L52J+oyB/pkDkfb9TkUeuPjDOwxiplNQe3omc5HSSfCvmMma5ag03o5JpU0P/t7R1DReuhBueLXPEP4qGbnasPeNP1+X7eGH1Qb48dJ2sQh1pR/yjaOzhwKL9V+m8dC8vfneI70766c0mqCxy89TcDIulTd2qesdb163KlUecZrz97C1a1a5KlftGqGbm5NJzwUa6zfuRyd/t4VZY0dk9lZaREiMXTzTB+h0n6mBfjNxrGYyi9GqMJjoYVYvumI1bjNmoeajavQjKijXDU5SO3Dw1N0NjaFNPvxO1dT1PrtyNfKRzbD/lS6s61ajiYFN8mNO+dG9eB3PTylevdL9HRcqoGleCoh7pHNvP3CxSRkeu36VxDTcWbT1G5w9/4MXFm/juwHnU983IrQykHj0CY2OUXnXJvXJO73Du1XMY1234SKdQ1vDGuG4j8m5cKTioUqHNzdEPmJONcT2fx01x2TM2xqReHbLO6A8EyTpzAZPGj1ZGKBQoLMzRJKfoDmVfvo5JvTqYNMhvgyg93DFr+zSZJ06XWNLLjMoYs0bepB+/pHc47fhFzJvVLybSwylMVGjva7dps3KwaNHgX5+zPFWtXgVnVydOHjmjO5abk8v5U5do2vLB14aFpTn7z//GwUs7+frHz6nXqM4Dw1cWNp7OWLrqt/s1OXmEP6Td71ZMu9+tmDhGKiV1BzzDzc0Fzw+VpvkzcvMK1TGtRos6R437009G+VYmGq2i3F5PolKZb16vXj2uXr3K4cOHuXbtGjExMZiamgLw+eefs337drZu3cprr73GsGHDWL58OfPnzwfAz8+PCxcusH79eoPn/vTTT2nRogUrVqzQHWvYMP+PbHp6Ol9++SWHDh2iTZv8HjsvLy+OHz/O6tWrDe4ZkJ6ezvTp01EqlXqfDx06lDFjxui9Hz16NBMmTABgypQpnD59ms8//5xOnTrplvOxs7PTW+7o448/5oMPPmDUqFG69MyfP59p06Yxe/ZsDh48yNmzZ7l58yZ16tTRhfmbra0tCoXigUsoHTx4kKtXr3L37l2qVasGwIYNG2jYsCHnzp2jZcuWQH4HxNq1a7G2zn/AMGLECP78809dB8X9srOzyc7WnwqVq1WjUiiLTYshpg7WGBkryY5N1jueFZuMmbPhEaBmzrZk3Rc+OzYZI5Uxpg7WZMUkYeb8f/buOzqq4m3g+Hc3u+m9h9ACoZPQe0fpSK8CgiBFwYZgAUFAFAV/Viz4KoIdQUBAOkjvIfSSHiC997q77x/BDZtsAkoqPp9z9hx2MnN37jB7986dZk92XKpBnOy4VINjJvoHc+aFr0gLjsbcxZYmLw2j9/Yl7On5GrlJ6f/oPKoCa1d7ADKKlE1GfAp2Dzkyy6GWC+0mPsbJb3Zx9PM/8GxRnwFLn0KTm8fFzcce6tjlxcrFHoDMeMPyyIxPwbaU8rB0sTeaxvJu3cnLyCbyXADtXxhGYlAEmXEpNBzaGfdW9UkOLXyg1/a5wWg1Wi6srfp7CPxN5WiLQmVCflyyQXhefDK2Lg7/+rhmtd0x7exK4tbDBE1ehrlXDWotn4HCxIToTzY8ZK7Ll/nd71XRa052XApWNUuuR+Yu9mQXqUfZ8SVf10rj+XhLunw5B5WFKVkxyRwY9z45iVXzGpWUmYNGp8PRytwg3MnKjPgSpu5GJGfifycRU5UJH45oT3JWLu/uuUhKVh5LB7XSxzkbHs/AZjVZPaYjtxLTWbH3Ehqtjpldq9douKSMbDRaHY7WFgbhTtYWxKfdf0m2uNQMjt+4zbsTDGcmebnZs2xcT7w9nMjIzuXno5eZsvoPNrwyijr/ot5VNQoLaxRKE3SZhr/tusw0FJbGH7Qp7VxQ1vCG/Dxyt38FFtaY9h6PwtyK3H3G7yXFoyMpI6vgu2ZjOGPGyeYBv2spGRy/Hs67T/UrMc7l8GiCohJ468neD53fyqC/HtkUuR7ZWBD/AEtE6stoUh+D8IiEVM4GRjCwTUNWzxzMrbgUVmw6jEajY2b/dmV6DuVN6tH9KWzsUJiYoE02XMZNl5yE0t6xhFQF7L7aiMLWDkxMyP5tHbkH/9T/Lf/iWcwHjyb/2kW0MZGofFqjbtelWgwqKUppb4dCZYIm0bCMNAlJmDuVXkZ/s5kwGoW5BZn7Cx9IZu37ixQHO1y/+aSg00ClIn3TH6St/7VM818RVA532yHxyQbhmvhkVA/RDsk4eh7HqcPIPHuF3PAorDq3wObxDqD8Z88tqgpnl4I9uoqO8k+IS6RGzZKfC4UGhfHmC8sJvB6ElY0VE6eP5YftXzOy9yRuhd4u1zyXN8u77f6sIm2vrLgUbEppr1m62BdPE5+CVQn3zfX6tcXM1pIbGwv3WUgKiiL1dhydXhvLoTe+JS8zh5bTB2LlZo/V3XakENVVuXQK6HQ6FAoFfn5+pKen4+RkuPFgVlYWwcEF63eNGzeO+fPnc+rUKTp27MhPP/1Ey5YtadrUeK/uhQsXGD16tNG/Xbt2jezsbPr0Mbxpzc3NpVWrVgZhnTt3RqlUkpmZiYeHB+vWrcPHp7DXtW3btgbxr1+/zowZMwzCunTpwieffFJKSYCfnx9nz541ePCu0WjIzs4mMzOTCxcuULNmTX2HwL9x/fp1atWqpe8QAGjatCn29vZcv35d3ylQt25dfYcAgIeHB7GxJS8Ns2LFCpYuXWoQNsqqOWNsfEtIUTpd0R1BFApKHf9Z9I93Z44YHKfIMRUKw7Dog4UjUVJvQMK5IAae+pA6Y7oRuGbXg2e+kvgM68wT707Tv//p6VVA8aJBoXjoHVcUSiWRl0M4sOo3AKKvhuPasCZtJz1eZToFGg3rTO8VhZ1126Z8ABQ/dYVCcd/iKPb3ImW49+WveHzVdJ45uxptvobYK2Hc3HoSF5+6ALj61KXl0/34ZdCb//Z0Klex785D1iGlgvyEFG699gVotWRdDkbt5oDbzOFVrlOg7vDOtF9ZWI8OTSq5HpV+kTJWZP+uHKOPX2dnn4WYOVrjPaEX3dbMYfegJeQkpN4/cSUpOplPhw4FxkdQaHU6FAp494k22JgXjJCc91hz5m05yxt9fTFXm6DV6XC0MmNR/5aYKBU0dbcnLj2b9aeDql2nwN+KlxElltG9tp0NwMbcjN7N6xqE+9Zxw7dO4abLLeu6M+6j3/n12BVeG/7fWjJGr+CHn5zd30JuQadU3uFNmA6eAQd/AU313IRR/DPFvms6HuCbBttOX8fGwozevvVKjLP15DW8PZzwqVP2e5xVpKLXHp2ueLkZs+3MjYIy8jEsI62uoONz0diemCiVNK3lSlxKBuv/8q92nQJ/k3r0IIq35+53r5S2+Hkwt0DVoCkWE2agiY4g7/hBADK/+wzLmfOx/eR70IE2JoKcv3Zh1sv4kpbVQrE2Bg90b2jZtxe2M54ift5itEnJ+nCz1i2wnTqBpPc/JffKdVS1amD/ymxs4xNJ/fbHMs16hTHyXOBh2iHRb6/B490XqL/3K9BB7q0okjftx36U8aWKq5pBI/vx1qrX9O+fm/AKUPz5SUEbt+RyuuR3lUt+hRs2+5+5xMb965nwzGhWLPywjHNdvhoO60zP9wrbaztKaPfzb9r9lFyOTcf1IPyvi2TEJOvDtPkads38hN6rpjP9ytdo8zXcPnaVsIMXHuhcRNnSPaIj9itLuXQKXL9+HS8vL7RaLR4eHhw6dKhYHHt7e6DgwXSvXr34+eef6dixI7/88gszZ84s8dgWFhYl/k17d7rqn3/+iaenp8Hf/p6p8LcNGzboH5wX7bQAsLKyKhZWdDmjvzs/SqPValm6dCkjRhRfz87c3LzU83lQJeWjaHjRzZIVCoW+zIx54403mDt3rkHYjoYzSohdspzENLT5Gv1o3L+ZO9sWmz3wt+y4FMxdDXtvzZxt0ebl60f4Z8clG42THV/yQzRNVg4p129j41U9bshv7jtPxD2bJpvc3XDT2sWO9NhkfbiVky3p8cbL8kGlxSYTF2i4BE5cUARNBlSdhl3IvvNE31sed6fyWbnYkXlPeVg42RabCXCvzLjkYqMDLJ1sybyn7qSEx/L7mHdQWZhhamNBZmwyAz6fQ+qtgg2warRvhKWzLVNPFnYMKlUmdHtzAq2m9ue7Li8/1LmWl/zEVHT5GlSuhqNxVE525BUZtfNP5MUmocvTwD3XlOzAO6jdHFGoVejyjO9lURnu7D1PvJHvlYWrHdn31CMzZ9tiswfulR2XjEWRemR+n2tQSTRZOaSHxZAeFkPC+WCeOPYB3uN7cHX19n98rPLmYGmGiUJBQrrhrIDEjFycrMyMpnG2NsfV2kLfIQDg5WSDDohJy6KOozUu1uaolApMlAqDOPEZOeRptKhNqs+oQQcrc0yUChLSDDfaTkzPwsmm9N99nU7H1jM3GNS2AWpV6SPclEoFzWq5cOshr/9VhS4rHZ1WU2xWgMLSptjsAX2ajBR06cn6DgEAbWIUCoUShY0DuuSqvS+OeDgOVhYF37UiI94Lvmul77eh0+nYevoag9o1LvG7lpWbx57zgTw7sIPRv1cHhdejf1tG1xnUtlGxMnKxtUJlosTknhHdXm4OxKdmkpevue/1qyqRenR/urQUdBoNSntH7t1BS2FnjzbF+NKBf9PGFixTlXsrFKWdIxZjpug7BXSpKWSsehPUpihsbNElxmMxYQba2Adbtqkq0SanoMvXYOJkeI9t4uhQbPZAURZ9euKwaB4Jry8j58x5g7/ZzXqajJ37yPhjJwB5waEoLCxwWPAyqWt/euiBYRUpP+luO6TIrAATJ7tiswf+CU1iKndmLUdhqsbEwZb8mARcX32a3NsPtmRjZftr91GDh/mmd5cYc3Z1Ij42QR/u6OxQbPZAaXQ6HVcuXKe2V637R65iQved128EDIXtNcui7X5nW7JKaa9lxiXrVwO4N02mkfaajacTNbs2Z9eMj4v9Le5yGBv6L8TUxgKlWkV2Yhqjti0h9lLoPzsxIaqYMm9hHzx4kMuXLzNy5Ehat25NdHQ0KpUKb29vg5ezc+EUnwkTJrBhwwZOnjxJcHAw48aNK/H4vr6+HDhwwOjfmjZtipmZGbdu3Sr2efeOogeoVasW9evXN9ohYEyTJk04dsxwtPSJEydo0qRw7Tu1Wq3f2+BvrVu35ubNm8Xy4+3tjVKpxNfXlzt37hAQEGD0c01NTYsd09h537p1i9u3C6eEXbt2jZSUFIP8/VNmZmbY2toavP7p0kEAujwNSZdCceve3CDcrbsP8ecCjaZJOBeIW3fD9fLce/iQdDEUXb7mbpygYnHceviScNZ4WQIoTVXYNPAk656e36osNyObxPAY/SsuMIK02CTqdy08bxO1CXU7NOa2n/GyfFC3/QJwqudhEObk5UFKRNVZrzovI5uU8Bj9KzEggozYZGp3K6xbSrUJNTs0JqqU8og6H2SQBqB2dx+jafKzcsiMTcbMzpI63X0I2ecHwI3fj/NT3wX83H+h/pUencj5NX+yZdLKMjrjsqfLyyfzcjC23VoYhNt0a0nGuRslpLq/jHPXMavrbjDMzqxeDXJjEqtUhwBAfka2/gF8elgMKQERZMUk49HdsB65dWxMXAnXKIB4vyDci1zXPHr4lJrmQSkUCpRVdM1htYmSJu52nAyLMwg/HRZLC0/j0+Nb1nQkLj2bzNzCuhCemI5SAW53H5K3qOnIraQMtPc0bsMT03GxNqtWHQIAapUJTWq6cDLgjkH46YA7tKjrVkKqAueCo7gdn8rw9vff40On03EzMgHn+zy0qja0GrSxt1DWNrx3MandBG1UsNEkmshgFFb2oC7skFI6uKHTatGllf4QRlR/apUJTWq5cvKm4bIIp2/cooWXRwmpCpwLiuB2XArDO5a85vRe/yBy8zUMMrLhd3Whvx4VLaObt2lRt/RBMueCIrkdn8LwjsXbEy283LkVl4L2nn1fwuOScbG1rFYdAiD16IHk56MJuYnK13A2vdq3Lfk3r5aQyAgFoDItHp6Xiy4xHkxMUHfsQe7Z4w+X38qQn0/ujQDMO7QxCDZv34bcSyWXkWXfXjgufpXEN98l+/jpYn9XmJtB0f2VNBpA8WDTfaqSvHyyrwRh1cVwFQfrLq3IOn/9oQ+vy80jPyYBVCbY9u9M+v7qse9CZkYmt8Pu6F/BN0OJi4mnU4/2+jgqtYq2nVpx4azxzc9L0rhZA+Jjq057/kHlZWSTEhajfyUGRJARk0ytIu1+z/u0+6PPBxmkgYJ2f7SRNE3G9CArPpWwAxdKPF5uWhbZiWnY1XXD1bceoXv9/vnJCVGFPNRMgZycHKKjo9FoNMTExLB7925WrFjB4MGDeeqpp1AqlXTq1Ilhw4bx/vvv06hRIyIjI9m5cyfDhg3TL9EzYsQInn32WZ599ll69epVbJT/vd544w18fHx47rnnmDVrFqampvz111+MHj0aZ2dn5s2bx8svv4xWq6Vr166kpqZy4sQJrK2t9ev6/xvz589nzJgx+o2St2/fzubNm9m/f78+Tt26dTlw4ABdunTBzMwMBwcHFi9ezODBg6lVqxajR49GqVRy6dIlLl++zPLly+nRowfdu3dn5MiRfPjhh3h7e3Pjxg0UCgX9+/enbt26pKenc+DAAVq0aIGlpaV+Y+S/Pf744/j6+jJhwgQ+/vhj8vPzee655+jRo0exZZAqS8CaXXT47FmSLoaS4BdIvYm9sfR0IuT7gg6e5gvGYuHuwNkXvgIg+PsDeE/tQ4slEwj56S+c2jTAa3xPTj23Wn/MwG9203PLIhrNHkzkHj9q9GuDW7dm/DV0mT6O7+Inidx3nsw7CZg7F+wpoLaxIHzjUX0ctb0Vlp7OWLjZA2BTv+DmPzs2ucSZDJXp1Le76TZ7CAlh0SSGRtNtzlDysnO5/McJfZzhH84iNTqJAysLlm0xUZvg0qBgw0sTUxU27g64N62j73QAOPnNLqZtfotus4dwdcdpPFvWp82Tvdj+xrcVf5L/gP+3u2k3ewjJoTEkh0bTbs4Q8rJzubm1sDz6fjST9OgkTrxfsDTShbV7GLXxTdo8O5iQvX7U69uGWl2bsXHk2/o0tbv7oFAoSAqJwr6uG10XjCcpJIprvxWsL5idnE52suGa79o8DRlxySSHVO3RTbH/9wd1Pn6JzEtBZPjdxGlCP0w9nYn/cTcANV6bhNrdifCXP9ansWjqBYDSygKVkx0WTb3Q5eWTHVjQgI77fjcuUwZTc+kzxH33J2ZeHrjPGU3cdzsq/Pz+jRvf7KbZ80NIDYkhLTSa5i8MIT8rl7AthfWo0yczyYpO4sKK3+6m2UOfzW/SdPZg7uzxo2a/Nrh3a8beYYX1SGVpho1X4UNg61ouODSrTU5yBpkRCZhYmNH8xaHc2etHdkwypo42NJz8OJYeDtzaXrxhWFVMau/Nwu1+NHO3x9fTkd8vhBGVmsWoVnUB+PTQNWLTslj+REHDeGDTmvzf8Zss/tOfZ7s1Ijkzl4/+uspQ3zqYqwseHo1p5cWvfiGs3HeZ8W3rEZ6YzrcnAxnf1quyTvOhTOruw8Jf/qJZTRd867rx+6nrRCWlM6pTwYOjT/88TWxKBsuLrC+99cwNfGq74u1RvIPlqz3n8K3jRm0XO9Kzc/nl6BUCIhJ4Y0TXCjmnipB/fj+m/Z5GGxOONioElU83FDaO5F8quPaquwxDYWVP7t51AGhunkHXYSCmfSaTd2o7Cgtr1N1Gorl6/D+3dFBmZha37kTq30dExnAjIBg7Wxs83F0rMWfla1Kvliz8YR/Narni6+XO7yeuFnzXuhY8BPh02wliU9JZPqmvQbqtJ6/hU8cN7xolDxDaevIqvXzrYW/18DN7K9Okni1Z+NN+mtVywbeuO7+fvEZUUhqjuhTsy/bp9pMF16OJhkttbD19t4w8ipfRmC7N+fXoZVZuOcr4br6ExyXz7T4/xnf/d8uMVjapR/eXvWMjVs8vQBN8k/yAq5g9/gRKZzdy924DwPzJ6SgdnclcvQIAs37D0MbHoIm4BYCqsQ/mQ8aSvWuL/pgm3k1QOjqjCQtC6eiM+ZgpoFCQ80f1Wy8fIO3nTTgtfZ3cawHkXL6G9fBBmLi7kv57wcxPu9nTMHFxJnHJ+8DdDoGlr5P8v8/JuXIN5d1ZBrrsXHQZGQBkHT2JzZOjyL0ZRO7V66hqemI762myj54wmKFbXSSs3YLnB6+QfTmQTP8bOIzrj7qGC0k/F8yEcJ03GZW7E5HzCpe7MWtSsDSX0soClaMdZk3qocvLIzeooB1i0aIRKjcnsq+HoHZzwuXFJ0GhJP7r3yv+BMvID19vYPqLk7kVcpvw0NtMf3Ey2VnZ/Ll5rz7Ou58tJjY6jo/f+RKAZ1+ZxkW/K9wKvY2VtRUTp4+hUfOGLH/jA30aC0sLanvV1L/3rF2DRs0akJKcSnRE1Z5ZcfHb3bSdM4SUsIJ2f9s5Q8jPziXgnnb/4x/NJCM6iZN32/0Xv93DiE1v0vqedn/Nrs3YPOJtw4MrFDQe050bm46i0xT/XtUf1J7shDTSIuNxalyLbksmEbrnHLePXCnXcxbFPaob/laWh+oU2L17Nx4eHqhUKhwcHGjRogWffvopkydPRnl3KunOnTtZuHAhU6dOJS4uDnd3d7p3746bW+FDEltbW5544gk2btzI2rVrS/3Mhg0bsnfvXhYsWED79u2xsLCgQ4cOjB8/HoC3334bV1dXVqxYQUhICPb29rRu3ZoFCxY8zKkybNgwPvnkE1atWsULL7yAl5cX3333HT179tTH+d///sfcuXP5v//7Pzw9PQkLC6Nfv37s2LGDZcuWsXLlStRqNY0bN+aZZ57Rp/v999+ZN28e48ePJyMjA29vb9577z2gYO+DWbNmMXbsWBISEnjrrbdYsmSJQd4UCgVbt27l+eefp3v37iiVSvr3789nn332UOdclu5sO4WZgzVN5w7H3NWe1Jt3ODpxFZl3CnqtLVztsfQsvJnOvB3H0YmraLl0IvWn9CE7Jgn/Rd8T8edZfZyEc4GcmrWa5q+Ppvmro0kPj+HUrM9IvGdZEAsPRzp+MQczRxtyElJJOB/EgcFv6T8XoEbfNrT/pHDJqk5rngfg6ge/c+1/m8utTP6t41/tQG1uyqDlU7CwteLOhWB+mPgeufds8GlXwwndPSNKbNwcmLXrXf37LjMH02XmYMJOXmPduIL9LiIvhbBhxsc89tpYerwwnKQ7cexe+iOX7/mRrYr8vtyBytyUXu9MwczWkugLwWyd8D5595SHTQ1ng/KI8gtk15zVdJo3mk6vjCIlPIZds1cbTFE0s7Wk82tjsHZ3JCclg6CdZzixaiPa/NJn7lQHSduPYeJgg/uLY1G7OpJ9M5zgycvIjSgY+a12c8C0yEbNTfZ8rP+3la83jsN7kHM7hqudC5YUy4uKJ2jCW9R8axrOez8hLyaB2LXbifmi6n2HjLn2+Q5MzE1pv2IKpnaWxPsHc3D8++TfU4+sPA3rUfy5QI49u5oWr43Gd/4o0sNjODZrNQn3XIMcW9Sjz+8L9e/bLJ0IQPCGI5x6+Wt0Wi223h50H/1iwXUqKZ2EiyHsHb6clADD5byqkn5NPEnOymXN8ZvEZ+Tg7WzD6tEdqWFX0Gkdl55NVGrh0jmWpiq+GteZ9/ZdZsK6I9hZqOnb2JPZ3QtHn7rbWvDl2M58cOAKo7/9C1cbc55sW4+nOzao8PMrC/1aeZOcmcOafX7Ep2bi7eHI6mcGUMOxYG+fuNRMoop0LKZl5XDgUijzh3U2esy07Fze3nSE+NRMrC1MaVzDmW9nP4FP7Ufnga8m4Bx55laoOw5CYWmHLiGSnD9Wo0srmC6vsLJDYXtPh0leDjmbP8a05zjMxy9Al52OJsCPvBN/VNIZVJ4rNwKZ+nzhmsQrP/sagKEDHuedN1+prGyVu36tG5Kckc2aPWeIT8nA28OJ1bOeoIZjwTJUcakZRCUZ+a5dDGb+yG4lHjc8Ngn/kCi+fG5ouea/IvRr3YDkzGzW7DlHfOrdMpp5bxllEpWUZpCmoIxCmF9Cp6O7gw1fzhrCB1uPMXrlr7jaWfFkD1+efqx1uZ9PeZB6dH95J/4iy9oW81GTUTo4orkdSvq7r6GNL3iQqHRwQul8z2w4hQKLJ2egdHUHrQZNdCSZP31N7r7CpREVpqZYjJ+G0rUGuuws8vxPkfHZu+gy04t+fLWQte8QyXa22D4zCRNnR/KCw4h/6Q000QVL2Zk4O2FyTyet1YjBKFQqHF57EYfXXtSHZ+zYQ+LSgpnHqWt/BJ0Ou2efxsTFGW1yMllHT5HyRdUeuFWS1D+PYmJvi/Pz41G5OJITGM6taW+RF1nQDlG5OqL2cDFIU39H4XMNC58G2A3tRe6dGIJ6FKw5rzBT4zp3Eura7mgzskg/fI6IV/6HNi2j4k6sjK1d/QPm5ma8+f58bO1suHT+KjPGvkhmRuEyZx6e7gaztWzsrFnywes4uzqRlpbOjcsBTBk2iyv+1/RxmrdswndbvtC/f23ZSwBs/fVP3nyxyIPyKub83XZ/j+VTMLOzJOZCMH8Ubfd7OhvsFxDtF8ie2avpOH80HeYVtPv3PGfY7geo1a0ZtjWdub7hMMZYudrTdfEELJ3tyIhN5ubvxzj7yRajcYWoThS60nYqEaKIjR4TKjsLVd5VIzNihSEH6d29r266tPtH+o+7rrG5f6T/uBHv1KjsLFRtLtVjf5nKpAu6WdlZqPLUE1+7f6T/uHy/nZWdhaqvGo74rXDK6rWUXGXI/mZTZWehyksPq17LW1WGtETzys5ClTcmPeH+kf7jZpl6V3YWqrw5t6vpZuGV6FSN4vu1VpSOkdVjwOM/IXdWQgghhBBCCCGEEEIIIcR/xEMtHySEEEIIIYQQQgghhBBClCfZU6BsyUwBIYQQQgghhBBCCCGEEOI/QjoFhBBCCCGEEEIIIYQQQoj/CFk+SAghhBBCCCGEEEIIIUSVpZPlg8qUzBQQQgghhBBCCCGEEEIIIf4jZKaAEEIIIYQQQgghhBBCiCpLW9kZeMTITAEhhBBCCCGEEEIIIYQQ4j9COgWEEEIIIYQQQgghhBBCiP8IWT5ICCGEEEIIIYQQQgghRJWlQzYaLksyU0AIIYQQQgghhBBCCCGE+I+QmQJCCCGEEEIIIYQQQgghqiytrrJz8GiRTgHxj8hO3/eXpNBUdhaqPFetXHruR62Sb9v9pFV2BqoBXWpqZWehSlPU9q7sLFR9JiaVnYMqL99vZ2VnocpTtRlY2Vmo8vKPb6rsLIhHgNJcrtn3o7aQttr9mKilHXI/CTlyj30/Keby9FaIqk6ezAkhhBBCCCGEEEIIIYSosrSyp0CZkj0FhBBCCCGEEEIIIYQQQoj/COkUEEIIIYQQQgghhBBCCCH+I2T5ICGEEEIIIYQQQgghhBBVlk6WDypTMlNACCGEEEIIIYQQQgghhPiPkJkCQgghhBBCCCGEEEIIIaosbWVn4BEjMwWEEEIIIYQQQgghhBBCiP8I6RQQQgghhBBCCCGEEEIIIf4jZPkgIYQQQgghhBBCCCGEEFWWbDRctmSmgBBCCCGEEEIIIYQQQgjxHyEzBYQQQgghhBBCCCGEEEJUWbLRcNmSmQL3WLduHfb29pWdDSGEEEIIIYQQQgghhBCiXFS7mQIKRenrR02ePJl169aV2+dnZGSwbNkyNm7cSGRkJDY2NjRr1ox58+YxePDgcvvcshQWFoaXlxf+/v60bNmyXD7De/LjNHpuEBau9qQEROC/+AfiT98sMb5Lp8a0XDIRu4aeZMUkc+OLHQR/f8AgTs1B7Wj+6mis67iSHh7L5fd+I2LXuX/0uWbOtrR4czzuPXxQ21kSd+oG5xeuJz00Rh/Hqo4rLd96Euf2jTAxVRP110XOL1xPTnxqGZXOw+v/0ig6j++NhZ014ReC2LRoLdGBd0qM32lcb9qN6I5Ho5oA3L4cyo5Vv3LrYrA+zuPPDaVFv/a41q9BXnYuoecD2P7ez8SGRJX7+Tysdi+PoOmEXpjZWRHjH8yRN9eRFBBRapp6A9rRft4o7Oq4khIey+lVGwndbVifrNwd6PTGOGr38sXE3JSUkGj+mv9/xF0OA8DC2ZZOC8ZRq7sPpraWRJ2+ydFF60kJizHyiVWL48SBuMwYgcrVgZyAW0S+/X9knr1mNK7KxQGPhdOw8KmPad0aJKzbTtTb3xSL5/T0EJwmDkBdw4X8xFRSd50geuV6dLl55X06ZaLtyyNocrcexfoHc/QB6pHXgHa0u6cenVm1kTAj9ahDkXp0aP7/EX+3Hs26/aPRY59c/gsX1/xZJudWHlS+PVC17oPCyg5dQiS5RzaijQwqOYGJCnX7QZg0bo/C0hZdejJ5Z3ehuXYCAIWjB+pOT6B0rYPS1oncw7+Rf+FgBZ1N+dhw+ALr958lPiWD+h5OzB/di9beNY3GXfT9brafulosvJ6HE5sXTQEgT6Nh7Z4zbD91ldjkdOq6OfLisG50aeZVnqdRrn67fIf158OJz8ylvqMV87o1oHUNhxLj52q0fH0mlD8DoknIyMHN2pxpbesyrGkNADZfjWDHjSiCEjMAaOJiw/Od6tPcza5Czqc8bDh6ifUH/IlPzaC+uyPzR3ajdX1Po3EX/biP7WduFAuv5+7I5gUTAJj26Wb8gopf27o2rcPqWUPKNvNVzLkLl/nu501cuxFEXEIin6xYxGPdO1d2tirEhhPXWH/4MvFpWdR3s2f+kI609nI3GnfRhiNs9wssFl7PzZ7Nr4wsFr77QjCv/3yIns1q8/HkPmWe94oiZXR/po8NwWzQGBR2Tmgjwsj68Qs0AZfvm86kQTOsFn6E9k4o6W/O1Ierew7EtGtfTGrWBUATGkD2xm/RhJTcbqzqLIYNxWr8OEwcncgPCyX1s9XkXTJeRmofH2xmzUBVuzYKc3M00TFkbttG5sZN+jiqunWxnvY06oaNMPFwJ/Wz1QZ/r47sxw/CYdooVC6O5AaFE/vuGrL8it8DAVj36Yz9uEGYNamPwlRNblA48at/JPPYeX0cu9H9sR36GGYN6gCQfTWI+I/WkX05oELOpyK98vpsJk4ejZ29Lf5+l3hj3nICbpRy/w3Y2tnw+psvMvCJPtjZ23I7/A5L3lzFwX1HKijX5afbSyNo9WRvzO2siPQPYveidcQHltx+c27gSY9XRuHe3Av7Wi7sXfoDZ9fuLjF+5+eG0Ou1sZz5dhf7lhlvs4mKJTMFyla16xSIiip8QLlhwwYWL17MzZuFNw0WFhbl+vmzZs3izJkzrF69mqZNm5KQkMCJEydISEgo188tK7m5ueX+GbWGdKTlskmcf+M74s4G4D2pN91/epXdPV4lM6J4OVnVcqH7j/MJ+ekvTs35Apd2DWm94mlyElK58+dZAJzaeNPpq+e5snITd3adpeaAdnRe8zwHhi4j0T/4gT+363dz0eZrODblQ/LSs2g0cwA9f1vAru6vosnKwcTCjJ6/vk7ytVscGvUuAM1fG0W37+exf9BboNOVe/ndz2OzhtBr2kB+mvclcaFR9H1+BM/9uIB3es8lJyPbaBrvjk05v+04oecDyMvJ47GZT/DsDwt4r888UmKSCuJ0aMLRH/Zy62IwSpWSwfPG8ez3C1jRZx65WTkVeYr/SKtnB9Ni+gAOzl1Dcmg0bV4YypCfX+fnHvPJK6E83Fp70/eLOZz5YBMhu89Rr39b+n4xhy0j3ib2QkF9MrOzZPjmxUScvM6Op1aRFZ+KbR03clIz9ccZ8M3LaPM17Jr2EblpWbSYPoAhv7zBL71fI78Kl5ndoK54LHqGyMVfkXnuGo5P9qfud0sI7DubvMi4YvEVpmryE1OI/fw3nKcONXpM+6E9cH9tMnde/ZRMv+uY1fOk5qoXAYhaXrwDoapp+exgfKcP4K976tHgn1/n1/vUoz5fzOHsB5sI3X0Or/5t6fPFHP64px6Z2lky7G492nlPPcq9px6tbz3b4Li1e7Wg56pnCNl1pvxO+CGZNGiDuvtocv/6BW1kMCqfbpgNnUP2j0vRpSUZTWM6YDoKSxty9/+ALjkOhaUNKAsnLCrUpuhS4skLPI9p99EVdSrlZs+5G6za9BcLxj1Gy3qebDp2idmfb2bzoil4ONoWi//q6F68OLSb/r1Gq2XMu9/Tp1VDfdjn247z55nrLJ7QBy93R05cC2Pu19tYP28cjWu5Vch5laU9gTGsOhrAGz0a0dLDnt+vRjBn+0V+f7IjHjbmRtO8uvsyiZm5vNW7CbXtLEjMyiVfW/jbfC4iif4N3WnhboepSsn68+E8+8cFfn+yA67Wxo9Zle05H8CqzUdZMLonLet5sOn4FWZ/uZ3NCybg4WhTLP6rI7vz4pDCh9wajY4x7/9Cn5be+rAPpw0kT6PRv0/OyGbs+7/Qp1WD8j2ZKiArK5tG3vUYNrAvLy9cXtnZqTB7LoSwavtpFgzrTMu6bmw6fYPZ3+5h8ysj8XCwLhb/1SEdeXFgW/17jUbHmI+30MeneAdkZFIaH/55htZe1e8adC8po/tTd+iJ+cTnyFr3KZrAK5j2GozV/BWkvT4VXUJsyQktrLCY+Tr5V8+jtDPs9FU1aUHeyYNkBV6FvFzMBo3F6tWVpL0xDV1SfDmfUdkz790L2+fnkPrhx+ReuYzlkCE4rFxJ/FOT0cYWLyNddhaZm7eQHxyCLjsbtY8PtvPmosvOJmv7jrsHNUMTGUX2X4exeX52sWNUNzYDuuP6xkxiln1O1vlr2I0dSM2v3yZ08Ezyo4q3Qyza+pB5wp/4j9ajSUvHbkQfan6xhPCxL5NzveB+27K9L2l/HiLW/zq6nFwcnxlNzW/fIWzwLPJjq8dzmgcx+8VpzHxuMi/NXkBwUBgvzZvFhi3f0LXdQDLSM42mUavVbNjyDfFxiUyf/BJRkTHU8HQnPT2jgnNf9jrNGkyHZwayfd5XJIZE0+X5YTz50xt81WseuSW039QWZiTdiuX6n6fps3hiqcf38K1Hqyd7EXMtvDyyL0SVUO2WD3J3d9e/7OzsUCgUBmFHjhyhTZs2mJubU69ePZYuXUp+fr4+fXJyMjNmzMDNzQ1zc3OaN2/Ojh07DD5jz549NGnSBGtra/r372/QEbF9+3YWLFjAwIEDqVu3Lm3atOH5559n8uTJ+jgKhYKtW7caHNPe3l4/gyEsLAyFQsGvv/5K586dMTc3p1mzZhw6dEgf/9ChQygUCv78809atGiBubk5HTp04PJlw1EGv//+O82aNcPMzIy6devyv//9z+DvdevWZfny5UyZMgU7OzumT5+Ol1fBzWqrVq1QKBT07Nnzn/43lKrRzAGE/nKIkJ8PkRYYif/iH8mKTKD+5MeNxq//1GNkRiTgv/hH0gIjCfn5EKG/HqbRrEH6OA2nDyDmyBWuf7aNtKAorn+2jZhjV2k4vf8Df651PXec2zbA77W1JF4MIS04Cr/Xv0NlaUad4Z0AcG7fEMtaLpx+cQ0pN26TcuM2Z15ag1Or+rh1bVqm5fRv9Zg6gL2fb+XSnrNEBdzhx1e+QG1hRpuhXUpM88NLqzn24z4iroUTGxzJr69/jVKhoGGX5vo4X01+jzObDhMdeIfI67f4af6XONZ0oZaRxk1V4jutP36f/UHI7nMk3rzDgZfXoDI3pcGwkkf+tXimP7ePXuH859tJDo7i/OfbiTh+jRbPFNanVs8+QXpUIn+98jWxF0JIuxNPxPGrpIYX3NDbebnj3qYBhxd8R+zFEJJDojiy8DvUVmY0GNqp3M/7YTg/M4yk3/aRtGEvOcF3iHr7G/Ki4nGcMMBo/LyIWKKW/R/Jm/9Ck2b8htOydWMyz10nZdth8iJiST/qT/L2I1j4ehuNX9X4TOvP+c/+IHT3OZJu3uHg3XrkXUo98nmmP3eOXsH/bj3yv1uPfIzUo0Ml1COArLgUg1fdvq2JOHGdtFvFG0ZVhar14+RfPY7m6nF0SdHkHdmILj0JlU8Po/GVdZpiUrMBOX+sRnv7Brq0BLQxYWijQvRxtDHh5B3bjCbgHDpNvtHjVCc/HPRjeGcfRnTxpZ6HE6+O7oW7vQ0bj1w0Gt/GwgxnOyv962p4NKmZ2QztVHid/vPMNab1b0+35vWo6WzPmO4t6dSkDt/v96uo0ypTP164xbCmNRjRzJN6jlbM79YQd2szNl42PvPteHgCfhHJfPZESzrWcqSGrQXN3exo6WGvj/Nu3+aM8alJIxcbvBysWNSrCTqdjtN3jHdWVXU//HWB4R2bMqJzM+q5O/LqyO64O1iz8ZjxUac2FmY421rpX1dvx5Calc3Qjk30ceyszA3inLpxG3O1ir4tq8f1+mF069SOF2ZMpk/Pku+ZHkU/HL3C8HYNGdGhEfXc7Hl1SEfc7a3YeOq60fg2FqY421jqX1fvxJGalcPQdoYdRxqtlgW/HObZPq3xNNLZWZ1IGd2f6YBR5B7eRd7hnWgjb5H90xdoE2IxfeyJUtNZTH2ZvJMH0AQVn5Ga9eUKcg9sQ3srGG3UbbK+/RCUClRNW5XXaZQryzGjyfpzJ1l//okm/BZpn61GGxeL5TDjg2ryA4PIPnCQ/LAwNNHRZO/bR+7Zs5j6+hbGuXGTtC+/IvvgQagms29L4zBlOCm/7yVl0x5yQ24Tt2INedFx2I8fZDR+3Io1JH67iewrAeSFRxL/0XpywyOx7tVBHydq/kqSf/mTnBsh5IbeIXrRJ6BUYtmpZQWdVcWY/uxTfPK/Nezcvp+b14N48dk3sLA0Z8SoklesGD9xBPYOdjw94XnOnvbnzu1Izpw6z7Ur1Xc2zt/aT+vP8dVbubn7HHEBd9j+yleozU1pNrTk9lvUpRAOvvsL17afIj+n5PaG2tKMoZ88x5+vfUN2SvXvQBGiJNWuU6A0e/bsYeLEibzwwgtcu3aNNWvWsG7dOt555x0AtFotAwYM4MSJE/z4449cu3aN9957DxMTE/0xMjMz+eCDD/jhhx84cuQIt27dYt68efq/u7u7s3PnTtLS0h46v/Pnz+eVV17B39+fzp07M2TIkGIzDubPn88HH3zA2bNncXV1ZciQIeTlFdwM+Pn5MWbMGMaNG8fly5dZsmQJixYtKrZ80qpVq2jevDl+fn4sWrSIM2cKRp/u37+fqKgoNm/e/NDn8jel2gQHXy+iDxs2VqMPX8a5rfERaE5tGxSPf+gSji28UKhM7sbxJvrwpWJxnNs1fODPNTFVA6DJKbyZ0ml1aPPycW7f6G4cFeh0aO+54dLm5KHVaPVxKpNTLVfsXB24cbSwLDS5+QSfvo5Xm4alpDRkamGGUq0iM7nkHzgLG0sAMpPT/32Gy5ltbRes3Oy5faTw/12bm0/k6Ru4tyl5xKNba2+DNAC3Dl8ySFO3T2viLoXQ98vnmeL/OaN3LafJ+J76v5uYFUy0KlqfNLkaPNo/+P9FRVOoVVg09yb9qL9BePpRfyzbNCkh1f1lnL2GhU99LFoUlKG6lhs2PduSdvDcfVJWPpuHqEd3itSjO0XqUZ279ajPl88z2f9zRhWpR0VZONtSu3dLbmw49K/Pp9wpTVC61kZ7y/BBiSb8OkqPekaTmNRrgTYmHHXbvphPew/zp5ai7joSTNQVkeMKl5ev4fqtGDo1qWMQ3rFJHS6GRD7QMbaeuEKHRnWo4VT4ECk3X4OZynCSp7mpCv/g0pe5qoryNFqux6bRqZajQXjHWo5cjE4xmuZwaBxNXW1Ydz6cvt8dY+gPJ/jwWCDZ+Rqj8QGy8zXka3XYmVW/upaXr+H67Vg6Na5tEN6xcW0uhj7Y0n5bT16jQ8Na1CjlYeTWU9fo16YhFtWwjMT95eVruB4RT6eGhktOdWzgycWwUkZ332Pr2QA6eNeghoPh7JQ1+y/gYGXO8Cpwj/wwpIwegIkKk7oNyb9seF+Xf8UPVYNmJSZTd+uH0tWDnC3fP9jnmJmBiQpdxsO3tSucSoW6YSNyzp41CM45exbT5iWXkcEhGnijbtac3AvGBxBUe2oV5s0akHH8vEFw5vHzWLR6wAF4CgVKKws0KSXXEYWFGQqVSalxqpvadWri5u7C4b9O6MNyc/M4efwcbTu0LDFd3wG98DtzkRUfvMmlgCP8deIPXpg7A6Wyej8KtK/lgrWrAyFHC9timtx8bp2+Qc1S2m8Pqv/bUwg6eIGw48aXtRKVR4ei0l6Poup9JSjinXfe4fXXX2fy5MnUq1ePPn368Pbbb7NmzRqg4CH4mTNn2Lx5M3369KFevXoMHjyYAQMKR8fm5eXx1Vdf0bZtW1q3bs2cOXM4cKBwbfuvv/6aEydO4OTkRLt27Xj55Zc5fvz4v8rvnDlzGDlyJE2aNOHLL7/Ezs6Ob7/91iDOW2+9RZ8+ffDx8WH9+vXExMSwZcsWAD788EMee+wxFi1aRMOGDZkyZQpz5sxh1apVBsfo3bs38+bNw9vbG29vb1xcXABwcnLC3d0dR0fDBvnfcnJySE1NNXjl6UpueAOYOtqgVJmQHWfYoM+OS8Hcxfh6vhYudkbjK9UqzO5OjTd3sSc7LrVInFT9MR/kc1ODIsm4HYfvgrGo7SxRqk1oPOcJLNwcMHezByDhfBD5mTm0eHMcJhammFiY0WLRkyhNlFjcjVOZbFwK8pBW5DzT4lL0f3sQT7w2npToRG4eL3kN0GFvTiL4zA2iAkreq6CyWd4958x4w/LIjEvB0rXk9aMtXezJKpImKz4Fy3vqqG1tF5pNfIyUsBh2TFzJ1R8P0m3ZUzQa2RWA5KAoUm/H0fG1sZjdrU+tnnsCKzd7LF3ty+YEy4GJgy0KlQn58ckG4fnxyaj/QR0qKmXHUWI+/Il6v71P84AtND7yDRknLxP3VdVf8/TvelSsTjxAPSpW94zUo6ZF6lGXZU/R8G49KqrRqG7kZWQTuqvqdqYoLKxRKE3QZRpek3VZqSisjD94VNo6o6zhjdKpBrk7viL3yEZMGrTGtNe4ishyhUtKz0Kj1eF4t3P1b062VsSn3n+0UVxKOsevhTK8i49BeKcmdfnhoB/hsUlotTpOXg/j0MXgBzpmVZOUlYdGp8PR0tQg3MnSjIRM40sdRqRmcSEqheCEdD4c6MO8bg3ZHxzLe4dLHu326ckgXK3N6FCr5H0KqqqkjBLqkY0F8SXM2rpXXEoGx6+HM7xTyQ+jLodHExSVwPBOVWM2pCh7SRnZBfXI2nCJ1YJ6lHXf9HGpmRy/eafYQ23/sBi2nr3J4lHGf8+qEymj+1PY2KEwMUGXajjrSpeShMLOeFtS6eaJ+djpZH25ArQPtgq0+djpaJPiyb9a/WbAKe3sUKhM0CYZlpE2MQllCe3tv7ls2ojb/r04fb2GzC1byPqz6u4p9TD07ZAEwzLKT0hG5fxgv9MOT49AaWlO2q6S18N3mfs0+TEJZJ7wLzFOdePq5gxAXKzhslrxsfG4ujqXmK5O3ZoMGtoXpYkJE0fP4uMPvmLmnCm8OG9miWmqA6u77e2MIs9FMuJTsHqINi1A0yc64t7ci79Wbnio4whRHVS7PQVK4+fnx9mzZ/UzAwA0Gg3Z2dlkZmZy4cIFatasScOGJY/itbS0pH79+vr3Hh4exN6z/l/37t0JCQnh1KlTHD9+nIMHD/LJJ5+wdOlSFi1a9I/y26lT4RIjKpWKtm3bcv369RLjODo60qhRI32c69evM3So4VTELl268PHHH6PRaPQzINq2bcu/sWLFCpYuXWoQNtKqOaNtfEtIcY+ia+/fZ4NoikVXFD9OkWMqFEY+p5TP1eVrOP7Mx7T73wxG3Pg/tPkaYo5eIfLABX2cnIQ0Tsz4lLbvPU2Daf3QaXXc2nqSxEuh6DQVv6VJm6FdGPvudP37NVPfL/hHsfM0ElaC3jOfoPWQLqwet4z8HONTUEcte5oaTerwyai3/k22y02DYZ3p+d5U/fs/p3xQ8A9j9ec+xVG8uBTo7glUKJXEXQrh9Pu/ARB/NRzHhp40m/QYN38/hjZfw56Zn9Br1XSmXfkabb6GO8euEn7wwr87uYpm5LvyMFtmWHVojsvsMQX7FFy4iVkdDzwWz8A1LpHYz6rWDVWDYZ3pfk892llKPbpvmRT7KhqvR2fu1qOEu/Wo6aTHCPj9WLHDNRrbg8AtJwxmoFRZxQqnlO9dwQWbnN1rIbdgjc+8IxsxHTQD/voVNNXgfP8FRZHfPp1Od9+fQ4BtJ69iY2FG7xaGy7m8OroXy37ay/Cl36FQQE1ne4Z0asa2k9V3FJOiyKgbXSnjcLS6gp+7d/o2x+bubK1cjZb5uy7zeo9GmKtMDOKvOx/O7oAY/m94a8yK/K06KVpndHfL4X62nb5eUI98jc/ggYKZBN4eTvjUMb6Zqnh0GK1HD3I9OheIjbkpvZsVznzKyM5l4S+HWDyyKw5W1W+vjpJIGT0AIz/9Rm+WFEosnltAzuZ1aKMfbICR6aCxqDv2IuPdVyCvGt8XGGuP3ud+MvH551FYWKBu2hSbmTPQRESQfeBg+eWxshm5h9Q9QEPEZlAPnOdMJGL2UjSJxmcVOk4bhe2gntx+6lV01Xi5pRGjB7PyoyX695PGzgIoVk4F7ZWSy06hVJIQl8j8F99Cq9Vy6eI13D1cefb5qXy08styyXt5aDasMwPfnaZ/v+HpVcYjKhQPtQ+kjYcjfd56il8mvVc92mP/QdpHc8B+pXmkOgW0Wi1Lly5lxIgRxf5mbm7+QJsQq9WGU6eNXWTVajXdunWjW7duvP766yxfvpxly5bx2muvYWpqajRN3gPe2BR9gFBanIKHC8UfOBRlZWX1QJ9d1BtvvMHcuXMNwrY1nFFqmtzENLT5GsyLjJQ2d7YtNor/b1lxKZgXGY1r5myLNi+fnKSCpWuy45KNxsmOT/1Hn5t0KYy9fRagtrFAaaoiJyGNx/9cSuLFUH2cmMOX+bPTXEwdrdHla8lLzWTIxc9Jr4T1va/s9yP8QpD+veruEkg2rvakxiXrw22c7UiLN16+9+o1fTB9Zg/jiwnvEHnjltE4I5dMofnjbfl0zBJSohMf7gTKWNi+82y4u4Er3F3uCbB0sSMzNlkfbuFsS2YJ9Q0gMy7ZYDT332my4gtHPmfGJpMYaLjMR1JQJPUGttO/j7scxm/9F2JqY4FSrSI7MY2R25YQeymUqkqTlIouX4PKpchGb052xWYP/BNur0wkectfJG3YC0DOzXCUluZ4vjuH2NW/VYlNuv8Wtu88MUbqkUWRemTubEtWGdSjpPvUo7+5t2+Eg3cN9j+3+h+dT0XTZaWj02pQWBmeu8LCptjsAX2ajBR06cn6DgEAbWI0CoUShY0DuuQHW56hunCwtsBEqSChyAj+xLRMnGxK/03W6XRsPXmFQR2aoi7yINvRxpKPZw0jJy+f5IwsXO2s+WTrUWo4lzyjpapysFBjolCQkGm4KXtiZm6x2QN/c7Y0xdXaTN8hAODlYIUOiEnPoY594Yj678+H8+25ML4a2oqGzsU35K0OHKz+rkeGswIS07NwKjJ7oCidTsfW09cY1K5xsXr0t6zcPPacD+TZgR2M/l08GhyszAvqUZER74npWThZl9420ul0bD0bwKDW3gb16HZiGpFJ6by4bp8+THv3d77N62vZOn8UtZyqz/r5Ukb3p0tLQafRoCiyUbDC1qHY7AEALCxQ1WuMSZ0GmD/1wt3IChRKJbbr9pKx8lU01y7oo5sOHI35E0+S8f58tLdDih+vGtCmpKDL1xSbFaB0sEebVHqbShMVDUB+SChKR0esn57ySHYK6NshzoZlpHKyQ5OQXGpamwHdcV/+EpEvvUvmyQtG4zhMHYnjzLHcnrqAnICwssl0Jdmz6yDnzxUuGWxqVnBv5OrmQmxM4WwBJxcn4uJK3kw5NiaOvLx8tPfM1gm8GYKbuwtqtfqBn1NVtsB95/nGv3j7zcrFjvR72m9WTrZkPMBzkZJ4+Hhh7WLHtB3L9WFKlQm1OzSm7eS+vNdgMjpt1WnXCvGwHqlOgdatW3Pz5k28vY1vlObr68udO3cICAgodbbAP9W0aVPy8/PJzs7G1NQUFxcXg82JAwMDycwsPs371KlTdO/eHYD8/Hz8/PyYM2dOsTi1axesJZuUlERAQACNGzfWf+6xY4YjTU+cOEHDhg0N9kkoytS04AdFoyl9KSAzMzPMzMwMwtSK0kfaafM0JF0Kxb17cyLuWf7CrbsPEXuMTwNNOBdIjb6tDcLce/iQeDEU3d11ghPOBeHW3YeAr3ffE8eX+LMB/+pz8+7e9Ft7ueHQoh6XVxZf4iQ3saBDwrVLU8ydbYnce75YnPKWk5FNTka2QVhKbBKNuvoQcTUMABO1CfU7NGH7ez+XeqzeMwbTd84Ivpz8LrcvG7/ZHrn0aXz7tWP1uGUk3ql6m5zmZWSTV6Q8MmKSqdmtOfFXw4GC/SVqdGjMyRUlj06POR9ErW7NufRNYX2q1d2HaL9A/fuocwHY1/cwSGdfz530O4ZTNgFy79Ynu7puuPjW48wHVXfJHF1ePllXgrDu2orUvaf04dZdW5K67/S/Pq7S3KzY1HCdRlswguwhR2yUtZLqUa1uzUkoUo9O3ace1SxSj2oWqUfRJdSjNCP1qMm4HsReCiHhuvEOuypDq0Ebewtl7SZogi/og01qN0ETYnwNXE1UMCYN2oDaDPIKHgIrHdzQabXo0qrnBrClUatMaFLbjZPXw+ndsnBd09M3wul5n823zwXe4XZcMsM7+5QYx0ytws3ehjyNhgMXAunTuuruY1IStYmSJq42nLqdSO/6rvrwU7cT6enlYjRNSw979gfHkpmbj+XdxmB4ciZKBbhZF96vrD8fzjfnQvl8SCuauVWfh25FqVUmNKnlysmbt+ndonAW6+kbt+jpU/Lof4BzQRHcjktheMeSlwXa6x9Ebr6GQe2q+VrnolRqlQlNPJ05GRhB7+Z19eGnAyPp2ax2yQmBcyHR3E5IZXiRvZK8XOzYNHe4QdjqPX5k5uQVbNBr9+8GJFUWKaMHoMlHExaAqnkb8v0Kl85VNW9D3nkjS+lmZZL2xjSDINPHhqBq2orMz5aijYsuDB84BvOhE8hY+Tqa0IByO4Vyl59PXsBNzNq2JedoYRvdrG1bso/9s+WGFWrjnePVXl4+2VcDsezcivT9hWvjW3ZuTfrBkyUmsxnUA/d3XibqlffJOHzWaByHqSNxenY8d555k5wrgUbjVCcZ6ZlkpBu2CWKi4+jesxNXLhWsHKFWq+nUpS3vvPVhicc5e8qf4aMHGQxcreddh+io2GrTIQCQm5FNbpH2W3psEl5dfYi5p/1Wu0NjDr7367/+nLDjV/m6z2sGYYM/mEFCcBQnv9wuHQLikfNIdQosXryYwYMHU6tWLUaPHo1SqeTSpUtcvnyZ5cuX06NHD7p3787IkSP58MMP8fb25saNGygUCvr37/9An9GzZ0/Gjx9P27ZtcXJy4tq1ayxYsIBevXpha1vQ8OzduzerV6+mY8eOaLVaXnvttWIzEAA+//xzGjRoQJMmTfjoo49ISkpi6tSpBnGWLVuGk5MTbm5uLFy4EGdnZ4YNGwbAK6+8Qrt27Xj77bcZO3YsJ0+eZPXq1XzxxRelnoOrqysWFhbs3r2bmjVrYm5ujp1d2Y0yvLlmFx0+e5bEi6HE+wVSf2JvLD2dCP6+YG8GnwVjsXR34PQLXwEQ/P0BGkztQ8slEwj+6S+c2zTAa3xPTt0zWjbgm9303rKIxrMHE7HHD89+bXDr1owDQ5c98OcC1BzcnpyENDIj4rFrUpvWb08iYvc5Yu7ZoNhrbHdSAyPJTkjFuW0DWi2bRMDXu0kLfrBN/crb4bW76DN7GPFh0cSFRtFn9nDysnLw+6PwZnPC/54jJSaRHSsLfhB7z3yCQXPH8P2Ln5F4Jw6bu6ObczKyyb07SnP021NpPbQL30z/gOyMLH2c7NRM8qrw1LlL3+6mzZwhpITFkBIaTes5Q8jPziVwa+GN5mMfzSQjOolTd5dwufTtHoZtepNWzw4mdK8fXn3bULNrM7aMeLvwuN/sZviWxbSeM4SgHadxa1mPpk/24tBra/Vx6g9qT1ZCGumR8Tg2rkXXJZMI3XOO20euVFwB/Avx32yl5odzybocSOb5GziO74+6hguJP+8CwG3+U6jdnbjzykf6NOZNvABQWpqjcrTDvIkXurx8coJuA5B64AzO04aRdTWEzAsBmNX1wG3uBFL3n3ngdWQr0+Vvd9NqzhCSi9SjoHvqUa+79ejvpYAuf7uHoZvepOWzgwnb60fdvm3w7NqMP4rUo2FbFtNqzhCCd5zGtWU9mjzZiyP31CMAtbUF9Qa15+TbpXfuVRX55/dj2u9ptDHhaKNCUPl0Q2HjQP7lgvVd1Z2HobC2J3fvOgA0N8+iaz8Q0z5PkXdqBwpzK9RdR6C5dqJw6SClCQpHj8J/W9ujcK4JeTnoUqpeJ+X9TOrdhoXrd9Gsjhu+XjX4/fglopLSGNWtBQCfbj1KbHI6y6cMMEi39cRlfOp64F2j+Pqwl0OjiE1Op1EtF2KT0/nqz5NotTqm9Ck+86Q6mNiyNm/uu0pTV1t83e3YfDWC6PQcRjUv2Ozz0xNBxGbksLxPwZr4Axq68X/nQnnrwHVmdfAiOSuPj48HMrRJDf3SQevOh/PFqWDe7ducGjbmxGcU/MZZqk30HQnVyaReLVn4wz6a1XLF18ud309cJSopnVFdmwPw6bYTxKaks3xSX4N0W09ew6eOG941nEo89taTV+nlWw97q/vPpH1UZGZmcetO4eytiMgYbgQEY2drg4e7aykpq7dJ3ZqzcMNhmtV0wbe2K7+fvkFUcjqjOhYMNPp011liUzJZPq6HQbqtZ2/iU9sFb3fDUb1malWxMBvzgo65ouHVhZTR/eXu2oTFrIIH95qga5j2GoTSyZXcA9sBMBszDaWDM1lr3gedDu2dMIP0utRkyMs1CDcdNBbzkVPI/OJdtPHR+pkIuuwsyDF8AFgdZP62EbuFC8i7eZPcq1exfOIJlK5uZP6xDQDrGdMxcXYm5d0VAFgOH4YmJob8WwUPf019fLAaN5bMzVsKD6pSoapbt+DfahUmzs6ovL3RZWWhiYioyNMrE0nrtuDx/jyyrwSSfeE6dmMGoPZwIfnXnQA4z52CytWJ6Nf/BxR0CHi8N4/Yd78i6+INTJz/riM5aNMLBl46ThuF04tPETXvffIiYvRxtJlZ6DKrXz0qyf99+T0vvDKD0JBwQoLDeWHuDLIys9m8aYc+zqdfrSA6MpZ3lxW049av/ZWpMybw9vsLWLvmR7zq1+GFuTP4ds1PlXUaZebMt7vpMnsISWHRJIZG03nOUPKyc7n6R2H77YkPZ5EWncShu/sDKNUmuDSoCRTMNrBxd8CtaR1yM7JJCo8hNyObuCJ7KuZl5pCVlFYsXFQO7SO64W9lqX6to1L069ePHTt2sGzZMlauXIlaraZx48Y888wz+ji///478+bNY/z48WRkZODt7c177733jz5j/fr1LFiwgMzMTGrUqMHgwYNZvHixPs7//vc/nn76abp3706NGjX45JNP8PMrPlr9vffe4/3338ff35/69evzxx9/4OzsXCzOiy++SGBgIC1atGDbtm36kf6tW7fmt99+Y/Hixbz99tt4eHiwbNkypkyZUuo5qFQqPv30U5YtW8bixYvp1q0bhw4deuAyuJ/b205h5mBNs7nDMXe1J+XmHY5OXEXm3ZGxFq72WHoWNlIzbsdxZOIqWi2diPeUPmTFJOG/6Hvu/Fk4CiDhXCAnZ63G5/XRNH91NBnhMZyY9RmJ90whu9/nAli4OdBqyUTMXOzIjk0mbONRrn10z00XYFPfA58FYzG1tybzdhzXPv2DgDW7yqx8HtaBr7ahNjdl1NtTsbSzIvxCEF9OetdgRoGDp7PBUlJdJ/VFZaZm6leGy0Ht+ngTuz/epI8D8MIGw30Efpr3JWc2HS6v03lo/l/uQGVuSvflUzCzsyTmQjDbJ7xvMBLcukh5RPsFsnf2ajrMH037eaNIDY9h33Orib1nSZnYiyHsnv4xHV8fS9sXh5F2O45jS3406GywdLWny+IJWDgXLDtz8/djnPvEsD5VRSl/HsPEwRbXF8ahcnEkJyCcsKlLyYsoePCqdnVEXcNwpG6DnZ/q/23p2wD7YT3JvRPDzW4F19fY1RtAp8PtlYmo3Z3IT0gl7eAZolf9UHEn9hAu3K1H3e7Wo9gLwewoUo9sPJ0NZjzE+AWyf/Zq2s0fTbu79Wh/kXoUdzGEPdM/psPrY2lztx6dKFKPALyHdASFgqA/Sh4lVZVoAv3Is7BG3WEQCktbdAmR5PyxGl1awfR4hZUdCpt7Hnrk5ZCz5RNMe47DfNwb6LLTC45xYps+isLKHosJb+rfK9v0Rd2mL5o7AeT8XvIIqKqqX9vGJGdks2bnKeJTM/D2cGL1cyOocXe5iLjUDKKSDJdbSsvK4YB/IPNH9zJ6zJy8fD7ffow78SlYmqnp2qweyycPwNayeq5X3a+BGynZeXx9NpT4jBy8naz5bHALatgWPKSOz8wlOq3wO2hpquLLoa14/0gAE387i525mj7ebszuWDhq/rfLd8jT6pi/+7LBZ81s58WsDqWPrq+K+rVuWFCP9pwhPuVuPZr1BDUc761H6QZp0rJyOHAxmPkju5V43PDYJPxDovjyuaElxnkUXbkRyNTnC0cArvzsawCGDnicd958pbKyVe76taxHcmY2a/b7E5+aibe7A6un9qWGQ8HSWnGpWUQlF61HuRy4HMb8IR0rI8sVTsro/vJOH0JhbYv5sEko7B3R3gkj44M30CUULAGotHdC6fTPOtfMHhuCQm2K1YtLDMKzN68nZ8v3ZZX1CpN98C8UtrZYT56M0smR/NBQkl57DW1MDAAmTk6YuLkVJlAosJ4xAxMPd9Bo0ERGkrbma7K2bddHMXF2xnntN/r3VuPHYTV+HLn+F0h88aWKOrUyk7brCCb2NjjPfhITF0dyA8O4M3Mx+ZEF9Ujl4oi6RmE9sh87EIVahdtbc3B7q3BVhZQt+4h+o+D+0P7JwShN1Xh++qbBZ8Wv/pGE1dX/4fffPv/kW8wtzFnxwWLs7G3x97vEuBHPkJFeuCqFZ00Pg6WCIiOiGTfiGZa++zoHjm8lOiqGb776kdUff2PsI6qVk18VtN/6L5+Cua0VEReC+WXiewYzCuxqOBmM7rdxc+CZXe/q33eaOZhOMwcTfvIaP457ByH+axS6B9nRRZSpsLAwvLy88Pf3p2XLlkbjHDp0iF69epGUlIS9vX2F5q80GzwmVHYWqrwTZqUvyySgkeaR6o8sF91U/34txP+K4/nVbx31ivbUPOvKzkKVpmje+v6R/uN096z5LIxTNCx5iR5RQNVmYGVnocrLP151lx4U1Ufupj2VnYUqLytc2mr3kxL735lB9m/1jA6v7CxUeXPsWlV2Fqq8heGPTqdVRdnq/mSlffaw6Ooxq/+fUFZ2BoQQQgghhBBCCCGEEEIIUTFkuK4QQgghhBBCCCGEEEKIKqvq71ZYvUinQCWoW7cu91u1qWfPnveNI4QQQgghhBBCCCGEEEL8E7J8kBBCCCGEEEIIIYQQQgjxHyEzBYQQQgghhBBCCCGEEEJUWVqForKz8EiRmQJCCCGEEEIIIYQQQgghxH+EdAoIIYQQQgghhBBCCCGEqLJ0lfj6N7744gu8vLwwNzenTZs2HD16tNT4hw8fpk2bNpibm1OvXj2++uqrf/nJD0Y6BYQQQgghhBBCCCGEEEKIMrBhwwZeeuklFi5ciL+/P926dWPAgAHcunXLaPzQ0FAGDhxIt27d8Pf3Z8GCBbzwwgv8/vvv5ZZH6RQQQgghhBBCCCGEEEIIIcrAhx9+yLRp03jmmWdo0qQJH3/8MbVq1eLLL780Gv+rr76idu3afPzxxzRp0oRnnnmGqVOn8sEHH5RbHqVTQAghhBBCCCGEEEIIIUSVpa3EV05ODqmpqQavnJwco/nMzc3Fz8+Pvn37GoT37duXEydOGE1z8uTJYvH79evHuXPnyMvLe8AS+mekU0AIIYQQQgghhBBCCCGEMGLFihXY2dkZvFasWGE0bnx8PBqNBjc3N4NwNzc3oqOjjaaJjo42Gj8/P5/4+PiyOYkiVOVyVCGEEEIIIYQQQgghhBCiDGgVlffZb7zxBnPnzjUIMzMzKzWNQmGYYZ1OVyzsfvGNhZcV6RQQQgghhBBCCCGEEEIIIYwwMzO7byfA35ydnTExMSk2KyA2NrbYbIC/ubu7G42vUqlwcnL6d5m+D+kUEKKMuevka3U/2srOQDWQnSv16H7cNVKT7kfhWbOys1C1xUdVdg6qPo2msnNQ9WnlWnQ/+cc3VXYWqjxVl1GVnYUqL//akcrOQpWXE5lf2Vmo8pKirCo7C1VeYpplZWehymts7VnZWajy7CpzSLd4ZGmpHvXK1NSUNm3asG/fPoYPH64P37dvH0OHDjWaplOnTmzfvt0gbO/evbRt2xa1Wl0u+ZQ9BYQQQgghhBBCCCGEEEKIMjB37ly++eYb1q5dy/Xr13n55Ze5desWs2bNAgqWI3rqqaf08WfNmkV4eDhz587l+vXrrF27lm+//ZZ58+aVWx5lKKoQQgghhBBCCCGEEEIIUQbGjh1LQkICy5YtIyoqiubNm7Nz507q1KkDQFRUFLdu3dLH9/LyYufOnbz88st8/vnn1KhRg08//ZSRI0eWWx6lU0AIIYQQQgghhBBCCCFElaWr7Az8Q8899xzPPfec0b+tW7euWFiPHj04f/58OeeqkCwfJIQQQgghhBBCCCGEEEL8R8hMASGEEEIIIYQQQgghhBBVluxfXbZkpoAQQgghhBBCCCGEEEII8R8hnQJCCCGEEEIIIYQQQgghxH+ELB8khBBCCCGEEEIIIYQQosrSVnYGHjEyU0AIIYQQQgghhBBCCCGE+I+QmQJCCCGEEEIIIYQQQgghqixdZWfgESMzBSrQunXrsLe3r+xsCCGEEEIIIYQQQgghhPiPqjYzBWJjY1m0aBG7du0iJiYGBwcHWrRowZIlS+jUqRMKhYItW7YwbNiwys5qicaOHcvAgQPL7HhhYWF4eXmVGmfw4MHs2LGDo0eP0rVr12J/79evH2ZmZmzbtq3M8lUS78mP0+i5QVi42pMSEIH/4h+IP32zxPgunRrTcslE7Bp6khWTzI0vdhD8/QGDODUHtaP5q6OxruNKengsl9/7jYhd5wqP0bExjZ4dhKOvFxbuDhx7+kMidvvp/65QmeDz2mg8HmuJdR0X8lKziDl6hYvv/Ep2THKZl0FZ6frSCFo+2QtzOysi/YPZu2gd8YERJcZ3buBJt1dG4t7cC/taLuxf+gNn1+4xiNNq4mO0nvgYdjVdAIgPvMOxT7YQcuhSuZ5LeWn/8giaTeiFmZ0VMf7BHH5zHYkBJZcRQP0B7egwbxR2dVxJCY/l1KqNhOwurE8KEyXt546g0bDOWLrakxGTzI2NRzj76R+gq1591i6T++MxaxhqVweyAm5z661vST9z3WhctasDtRZPwdK3PuZeHsSs/ZPbb60t8diOQ7pS/8tXSNp9mqBp75XXKZQrrymP4/3cYMxd7Um7GcHlxd+TUMr1yqlTY3yWTMKmkSfZMckEfr6dsHuuVx4D29HwxaFY13VDoTYhIySaoK92cnvTsYo4nXKx4dRN1h+7SnxaFvVd7Zk/qC2t67oZjbto03G2+4cUC6/nasfmF4cAEBSTzJcHLnItIoGo5AzmDWzLxC5NyvUcKsOGMwGsP3ad+PQs6rvYMX9AG1rXdTUad9Hmk2y/EFosvJ6LHZufH1TeWa00Kt8eqNr0RWFlhy4hktzDv6GNDCo5gYkKdYdBmDTugMLSFl16MnlndqK5dqLiMl2ONhy7zPqD/sSnZlLf3ZH5w7vSun4No3EX/XSA7WdvFAuv5+7A5tef1L9Pzcxh9c5THLwUQmpmDp6Otswd1pluTeuW12mUqw0nrrH+8OWC65GbPfOHdKS1l7vRuIs2HGG7X2Cx8Hpu9mx+ZWSx8N0Xgnn950P0bFabjyf3KfO8VzXnLlzmu583ce1GEHEJiXyyYhGPde9c2dmqEBv+8mP9ntPEp6RTv4YL88c+TuuGtUqM/+epK6zfc5pbsYlYW5jRuVk95o7ujb21JQBBEXF8ue0o18KjiUpIYd7Yx5j4ePuKOp1yYTFkGJZjxqF0ciQ/LIz0L1aTd9l4W0Hd3Aer6TNR1a6NwswcTUw0WTu2k/X7Rn0cs67dsHxyIiaenihMVORH3CFr429k799bUadU7hyeHITjMyNRuTqSExhOzDtfk3XuqtG4Nn07Y//kIMyb1ENhqiYnMJz4T38i49j5Cs51+XKf0g/P54Zg6upA5s3bhC5eR+rpktoh9ngtmYy1bz3M63kQ9c1OQhevM4jjNuFxXEb3wKpxwfc1/VII4St+Jt2/lHuHamDy3EkMfnIQNvbWXPe/wScLPyMsIPyB0vYa0pPFXyzk2O7jLHpmiT78ydnj6DagK7W9a5GTncPVc9f4+t1vuB1yp5zOomyVRxv/qRMfYVvLpVi6S+v3ceTN9QDMuf2j0WMfX/4L/mv+fIgzEqJyVZtOgZEjR5KXl8f69eupV68eMTExHDhwgMTExAc+Rl5eHmq1uhxzWToLCwssLCzK7Hi1atUiKipK//6DDz5g9+7d7N+/Xx9mbW1N165d+e6774p1Cty+fZv9+/ezefPmMstTiXkd0pGWyyZx/o3viDsbgPek3nT/6VV293iVzIiEYvGtarnQ/cf5hPz0F6fmfIFLu4a0XvE0OQmp3PnzLABObbzp9NXzXFm5iTu7zlJzQDs6r3meA0OXkegfDICJpRnJ124RuuEwXb99udjnqCxMcfCpy7WPtpB87Ramdla0WjaJbutfYV//ReVbKP9Sx1mDaf/MAHbMW0NiSDRdnh/KuJ9e5+te88nNyDaaRm1hRvKtOG78eYbHF080GictKpFD728gKSwGgOajujHq/+ayduDCUjscqqLWzw6m5fQB7J+7huTQaNq+MJShP7/Ojz3mk1dCGbm39qbfF3M4/cEmgnefo37/tvT7Yg6bR7xNzIWC+tT6ucE0n/gY+19eQ2LAHVx9vXjsfzPIScviUpFOlqrMcUgXai+ZSviCr0k/ewOXSX1p+OMirvR8gdzI+GLxFaYq8hNSifp0E27Tnyj12KaeLtRaPJm0U8YbO9WB59CO+Cx7iouvryXhbABekx6j08+vcaD7fLKMXK8sa7vQ6adXCf/xL87N+Ryndg1p8d5UchNSibx7vcpLTifg462kBUWizc3HvU9rWn08k5z4VGKrYcfbnkthrNp5jgVPtKdlHVc2nQ1g9vqDbH5xCB72VsXivzq4HS/2a61/r9FqGfPZDvo0r6MPy87Lx9PBmj7Na/PBn37FjvEo2HM5nFW7zrNgcFta1nZh09kgZv94iM1zBhkvt4FteLFPS/17jVbHmC920qdZyQ+pqjuThm1R9xhD7sGf0UYGo/Ltjtmw58n+YQm6tCSjaUwHTkdhaUvuvu/RpcShsLAB5aMxGXbP+UBWbTnGglE9aOnlzqYTV5m9Zjub33gSDwebYvFfHdGVF5/oqH+v0eoYs/JX+rTw1ofl5WuY9eU2HG0sWDWlP272VsQkp2NpZloh51TW9lwIYdX20ywY1pmWdd3YdPoGs7/dw+ZXRuLhYF0s/qtDOvLiwLb69xqNjjEfb6GPT/GBNpFJaXz45xlaexnv8HwUZWVl08i7HsMG9uXlhcsrOzsVZs/Za6zasJ8FE/rR0rsmmw77M/vTDWxeOh0PJ7ti8f0Db7No7Q7mjX2MHr4NiE1OY/mPu1m6fhcfzS7oXMrOzcPT2Z4+bRrzwW/7ix2jujHr2Qvr5+aQ9ulH5F25gsXgJ7Bb8T6JUyejjY0tFl+XnU3W1i3khwSjy85G7eOD7UuvoMvOJvvP7QBo09LI+OlHNLdvQV4epp06YfPqa2iTk8g9d7aiT7HM2QzsjtvCGUQv+YLM89dwGDeA2t8sI3jALPKj4orFt2zXnIzj/sT9bx2a1AzsR/ah1pq3CB39MjnXig+uqI6ch3bGa9kUQl7/htSzN3Cf1IemPy/gfPeXyY0o3g5RmqnJS0jl9iebqTFjsNFj2nVuRvzWY4SevYk2JxfP2cNo9usi/Hu8TG70gz8vqkrGPTeW0dNH8v7cD7gdcodJLzzJqp/f56keT5OVkVVqWjdPV55dNIOLp4q3MVp08mXr+m3cvHgTExMTpr32NCt/fo+nez1DdpbxdnJVUV5t/N8GL0ZpUnjf6NioJsN+eYPgHWf0YWtbzzY4bp1eLei96hmCd51BVCytorJz8GipFi2m5ORkjh07xvvvv0+vXr2oU6cO7du354033mDQoEHUrVsXgOHDh6NQKPTvlyxZQsuWLVm7di316tXDzMwMnU5HSkoKM2bMwNXVFVtbW3r37s3Fixf1nxccHMzQoUNxc3PD2tqadu3aGTxoB6hbty7Lly/nqaeewtramjp16vDHH38QFxfH0KFDsba2xsfHh3PnCnsgiy4f9Hf+fvjhB+rWrYudnR3jxo0jLS1NHyctLY0JEyZgZWWFh4cHH330ET179uSll17CxMQEd3d3/cva2hqVSlUsbNq0afz2229kZGQYnMO6detwcXFh0KDyH23YaOYAQn85RMjPh0gLjMR/8Y9kRSZQf/LjRuPXf+oxMiMS8F/8I2mBkYT8fIjQXw/TaFZhXhtOH0DMkStc/2wbaUFRXP9sGzHHrtJwen99nOiDF7ny/kYidp4z9jHkpWVxeNx73N5+mrTgKBLOB3F+4XocW9TD0tOpbAuhjLSb1p8Tq/8gYPc54gPusOOVNajNTWk6tORRXFGXQvjr3V+4vv0U+Tl5RuMEHfAn+K+LJIZGkxgazZFVG8nNzKZGa2+j8auyFtP6c+6zPwjZfY7Em3fY//IaVOamNBxWchm1eKY/t49ewe/z7SQHR+H3+XbuHL9Gi2cK65NH6waE7vUj/OAF0u7EE7zzLLePXMbVt/QZO1WN2/QhxP96gPhf9pMddIfbb60lNzIB16f6G42feyeOW299S8KmQ2hSM0s+sFJJvdUvE/HBr+Tciimn3Je/+jMHEv7LIcJ/PkR6YCSXF/9AVkQCXiVcr+o+9RhZdxK4vPgH0gMjCf/5EOG/HML72cKGS/yJ60TtOkd6YCSZ4bGEfLOb1Gu3cGrfqKJOq0z9cPwaw9t4M6JdA+q52vHqoHa421mysYTZFDbmpjjbWOhfVyMSSM3OZWjr+vo4zWs6M3dAG/r7eqFWVYvbk3/shxM3GN66HiPaeFPPxY5XB7bB3daSjWeLj1oGI+UWWbzcHjWq1o+Tf/U4mqvH0SVFk3f4N3TpSah8exiNr6zTDJOaDcnZ+hna2zfQpSagjQlDG/VoPDz54dAFhndowohOTann7sirI7rhbm/DxmNXjMa3sTDD2dZK/7p6K5bUrByGdmisj7P19HVSM7P5aNoAWtXzoIajLa3q1aCRp3NFnVaZ+uHoFYa3a8iIDo2o52bPq0M64m5vxcZTxked2liY4mxjqX9dvRNXUEbtGhjE02i1LPjlMM/2aY2no21FnEqV0K1TO16YMZk+PbtUdlYq1A/7zjC8awtGdGtJPQ9nXh3XB3cHWzYe9jca/1JIJDWc7XjysXZ4utjTqkEtRnVvxbXwwgFbzb1qMHd0b/q3b4paVW3G4pXIctQYsnbtJHvnn2huhZP+xWq0sXFYPDHUaPz8oEBy/jqAJjwMbUw0Ofv3kXPuLKY+vvo4eRcvkHv8KJpb4WiiIsna/Dv5ISGom/tU1GmVK6epw0netJfkjXvIDb5NzDtfkxcdh8OTxtvfMe98TeL/bSL7ciB54ZHEfbie3PBIbHp3qOCcl58aM58g5peDxPx8gKzACEIXryMnIgGPyX2Nxs+5HUfoou+I23gYTZrxdkjA7E+IXreHjKthZAVFEvTKV6BUYNet+tajUdOG8+Nnv3B01zHCbobx3surMLcw4/FhvUtNp1QqWfjZG6z73/dE3You9vfXJi5gz8a9hAWEE3w9hPfnfoB7TTca+jYwcrSqpbza+NmJaWTGpehfdR9rRXJYDBH33Efc+/fMuBS8+rbmzonrpN4q3rknRHVSLVrd1tbWWFtbs3XrVnJycor9/ezZglEE3333HVFRUfr3AEFBQfz222/8/vvvXLhwAYBBgwYRHR3Nzp078fPzo3Xr1jz22GP6WQfp6ekMHDiQ/fv34+/vT79+/XjiiSe4deuWwed+9NFHdOnSBX9/fwYNGsSkSZN46qmnmDhxIufPn8fb25unnnoKXSnLigQHB7N161Z27NjBjh07OHz4MO+9V7jcxty5czl+/Djbtm1j3759HD16lPPn/9n0wQkTJpCXl8fGjYVTNXU6HevWrWPy5MmoyvkmVak2wcHXi+jDlw3Cow9fxrmt8R8fp7YNisc/dAnHFl4oVCZ343gTffhSsTjO7Ro+VH7VthbotFpyU0p5+FlJ7Gu5YO1qT+jRwrLR5OZz6/QNarYpux9yhVJBkyc6orYwI+K88YdVVZVtbRes3Oy5daSwjLS5+UScvoFHKWXk3trbIA3ArcOXcL8nTeTZAGp2aYb93SUJnJrUxqNdI8L/ukh1oVCrsPKtT8rhCwbhqYcvYNW2sfFED6jGy2PIT0gh/tcD949cRSnUJtj7ehUbvR97+DKOJVxbHNs0ILbI9Sr20CXs77leFeXctRnW3h7El/DQqirLy9dwPTKRTt4eBuEdvWtw8QFvjLeeC6JDfQ9qGBnF+6jKy9dwPSqRTvWLlps7F28VHxlnzFa/YDrUc6eGkVkFjwSlCUrX2mjDrxkEa8KvofQw3hFiUs8XbUw46rb9MH/mPcwnL0PdbSSYVN7M0LKSl6/h+p04OjWubRDesXEtLoYVb+gbs/X0dTo0rEWNex5qH7oSim9dd1ZsOkLvN9cy8r1f+GbfOTRabZnmvyLk5Wu4HhFPp4aeBuEdG3hyMaz4yGVjtp4NoIN3DWoUmXmxZv8FHKzMGV5NO2/Fg8vL13A9PJpOTQ0HeXRs5sXFYONLarSo70lMUhpHLweh0+lISM1g//kbdPOpfoNpHohKhaphw2Kj93P9zqJu1vzBDuHdAHWzZuReulBiHHWr1qhq1iK3hCWJqhW1CvNm3sWW/sk45o9F6wdcHlGhQGllgSY57f5xqwGFWoW1bz2SDxm2nZIPX8SmXdlda00sTFGoTMhPTi+zY1Ykj9ruOLk5ce5w4cDGvNw8Lp66RLO2TUtN+9TLE0lOTGbnr7sf6LOsbAvuKVOreB0rzzb+vZRqExqN6ML1DYdLPKaFsy11erfk+oZD/+wkRJnQVuLrUVQthiyoVCrWrVvH9OnT+eqrr2jdujU9evRg3Lhx+Pr64uJSsP6Xvb097u6G64fm5ubyww8/6OMcPHiQy5cvExsbi5mZGVCw7M7WrVvZtGkTM2bMoEWLFrRo0UJ/jOXLl7Nlyxa2bdvGnDlz9OEDBw5k5syZACxevJgvv/ySdu3aMXr0aABee+01OnXqRExMTLF8/U2r1bJu3TpsbAoaIpMmTeLAgQO88847pKWlsX79en7++Wcee+wxoKDjo0YN4+vIlsTR0ZFhw4bx3XffMWXKFAAOHTpESEgIU6dO/UfH+jdMHW1QqkzIjksxCM+OS8Hcpfh0XAALFzuijcRXqlWYOdqQHZuMuYs92XGpReKklnjMB6E0U+O7cBzhW06Qn176tLzKYOVqD0BGkbLJiE/BrgxG+Lk0qslTW5agMlOTm5HN5pkfkxAY+dDHrUiWLvYAZMUbllFWXAo2NUsuI0sX++Jp4lOwuqc+nf9iO2Y2Fkw4tBKtRovSRMmplRsJ/ONk2Z1AOVM52hTcJMcnG4TnxSdje7d+/RvWbRvjMv4xrvaZ+3AZrGRmd69XOUW+YzlxKZiVcG0xd7UnNu5SsfhKtQpTRxtyYpMBUNlY0P/C5yhNVeg0Wi6+8R1xR4yP9q3KkjJz0Gh1OFqbG4Q7WZsTn37/acdxqZkcD4zk3THF97l5lJVYblYWxKdHlZCqUFxaFseDonh31KO7trfCwhqF0gRdpuFvuy4zDYWl8ZHaSjsXlDW8IT+P3O1fgYU1pr3HozC3Inff9xWR7XKTlJFdUGdsDJeedLKxIL60WVt3xaVkcPx6OO9OMlwHPyIhlbOBEQxs05DVMwdzKy6FFZsOo9HomNm/XZmeQ3nTl5G1kTJKu/99XFxqJsdv3uHd8T0Nwv3DYth69iYbXhpeltkVVVRSemZBPbI17HB1srEiPiXDaJqW3jV595khvLbmD3Lz88nXaOnZogGvjX80951Q2tmhMFGhTTJcikWblITS0bHUtE6/bkRpZw8mJmR8v47snYbrbyusrHDasAmF2hS0GtI++Zg8P+OzvKsTlYOt0Xvu/PgkrJwdHugYjtNGoLAwJ3Xn0XLIYcVT322H5BW5z86LS8H0bhuuLNR5cyK50YkkH6menUuOLgXfqaQidScpPgk3z5KXs2vethkDx/Xnmb6zHviznls8i0unLxN2M+zfZLXClGcb/171+rXFzNaSGxuPlHjMxqO6kZeRTfCu6n+dEqJadApAwZ4CgwYN4ujRo5w8eZLdu3ezcuVKvvnmG/2DbmPq1Kmj7xAA8PPzIz09HScnw6VhsrKyCA4uWFMsIyODpUuXsmPHDiIjI8nPzycrK6vYTAFf38Kpj25uBRdnHx+fYmGxsbEldgrUrVtX3yEA4OHhQezdNRlDQkLIy8ujffvCDans7Oxo1Oif96JPmzaNvn37EhQUhLe3N2vXrqVLly6lHisnJ6fYzIw8nQa1wvjI1/sqOmNCcZ/FwIpFVxQ/TpFjKhRGPucBKVQmdPpqDgqlAr/X1/2rY5S1ZsM60//dwo6b357+AChWNAVlUwb73CaERLF2wELMbC1pNKAdg/83kx/HLq/SHQMNh3Wm53uFZbRjyt0yKl5I960axf+uMJjp02BIRxqO6MLe578gMeAOzk3r0G3JxIINhzdVr5t1Y+Xzb787Sitz6n32EmHzvyQ/qWqPMnlQxcuHUr9jxWaEGble5adn89djb6CyMselWzN8lkwkMzyW+BPVb7YA3HNNvkunKyim+9nmH4yNuSm9mzy66+KXpmgZ6dAVK0tjtvmHFJRb45rlk7HqquCHn5zd30JuQadU3uFNmA6eAQd/AY3xJfOqEwVGvmsP8GXbduYGNhZm9PapZxCu1RU8RF80ticmSiVNa7kSl5LB+r/8q12nwN+KlscDl9G5wILvVbPC/U0ysnNZ+MshFo/sioOVeSmpxaOmWD1CV2I9Co6MZ+Uv+5jxRBc6N6tHfHI6H206yDs/7mbJlEd3I3ij7nP/mPTS8ygsLFE3aYr19BloIiLI+atwVqkuM5OkGc+gsLBA3bo11s8+hyYqkryLF8o54xXE2D3iA9xz2w7ugcvzE7j97DI0iSn3jV+tFCuTf90MKcZz9lCch3Xhyogl6EpYNreqeXx4b+a+95L+/RuT3wSMty90JTRILKwsWPDpa3zw6kekJqUajVPUi8ufp34TL54fUXzvxcpWkW38ezUd14Pwvy6SEZNc4vGaju1BwJYTaKpJ/XrUPKoj9itLtekUADA3N6dPnz706dOHxYsX88wzz/DWW2+V2ilgZWU44kOr1eLh4cGhQ4eKxf17vf/58+ezZ88ePvjgA7y9vbGwsGDUqFHk5uYaxL930+K/G/TGwrSlTMcuuvGxQqHQx//7QlX8wcs//8V8/PHHqVOnDuvWrePVV19l8+bNrF69utQ0K1asYOnSpQZhI62aM9rGt4QUxuUmpqHN12BeZBSyubNtsdkDf8uKS8Hc1bD31szZFm1ePjlJBdMAs+OSjcbJjn+wH8F7KVQmdP76eaxrufDX6HerzCyBwH3niby7aTKAiWnBV9baxY6Mu6OPASydbMmIf/ibRW2ehqTwgrXgoy+H4tGiHu2e7s/uBWsf+tjlJXTfef0mQVBYRpYudmTeU0YWzrZklVDfADLjkrEsMmLAwtmWzHvqU+eF4zn/xXYCt50CIOHGHWxqOtNm9hPVplMgPzENXb4GdZHROGonu2Kjdh6UWV13zGq70WDdgsJAZcF1q234Ji53n0NO+IMtdVHZcvTXq6LXFjtySviOZccmF7u+/X29yk26Z9qyTkfG3Y28U66GY93AkwbPD612nQIOlmaYKBUkFBmFm5iRjZN16Q/QdDodW/2CGdTSC3UJSys9qvTlVmQ2RWJGNk73efCo0+nYej6YQS3qPtLlpstKR6fVFJsVoLC0KTZ7QJ8mIwVderK+QwBAmxiFQqFEYeOALvnBlpCpihyszO9+1wxnBSSmZ+FkY1lqWp1Ox9bT1xnUtlGxOuNia4XKRInJPZsxe7k5EJ+aSV6+poam25EAAQAASURBVFrVscIyKnI9Ss/CqcjsgaJ0Oh1bzwYwqLW3wTnfTkwjMimdF9ft04dp7957t3l9LVvnj6KW039nj4H/Agdry4J6VGRWQGJaJk62xpdrW7vrBC28azKlX8HG3g1rumJhpubplT8ye1gPXOwfreXxtCkp6DT5KB0MZwUoHRzQJhnfBF6fNrrgHlATGoLSwQGryVMMOgXQ6dBERgCQHxyEqnYdLMdPIKWadwrkJ6Wiy9egcjGcFaBysic/IbnUtDYDu+Px7otEvLCCzBMXyi+TFSzv73ZIkftmtbMdeUVGxf8bNZ4dQs0XRnB1zDIyr4c/9PEqyvG9J7nmf0P/3tS04BmRo4sDibGFs3McnOxJijP+fatRpwYetT1497u39WGKu+2x/WG7earH00Tes+fJ82/PpnPfjrw48hXiox5sGcuKVJFt/L/ZeDpRs2tzds34uMTjebRvhIN3DXY/V/qzNCGqi2qxp0BJmjZtqt88V61Wo9Fo7pumdevWREdHo1Kp8Pb2Nng5OxdMOzp69ChTpkxh+PDh+Pj44O7uTlhYWHmeilH169dHrVZz5kzhjuapqakEBv7zNd4VCgVPP/20fjkipVLJmDFjSk3zxhtvkJKSYvAaZt3sH3+2Nk9D0qVQ3Lsbrjfp1t2H+HPGzyXhXCBu3Q03BnLv4UPixVB0+Zq7cYKMxPEl/mzAP8rf3x0CNl7uHBq7wvAhXiXLzcgmKTxG/4oPjCA9Npm6XQvLUqk2oXaHxtzxK/u1/xUKhf4HuKrKy8gmJSxG/0oMiCAjJpla3QzLyLNDY6JKKaPo80EGaQBqd/ch+p40agtTdFrDTjmdRqu/4aoOdHn5ZFwKxq57C4Nw2+4tyDh3o4RUpcsOiuBK7xe52neu/pW89yxpJ65wte9cciOr3o1mSXR5GpIvheLSw/Da4tKjOYklXFsS/QJx6WFYd1x7+pJ8z/XKGIUCTMyq9vfLGLXKhCY1HDkZZLjkzemgKFrUdikhVYFzoTHcTkhjeNtHdM3lUqhVJjTxcORksGEH2engaFrULn35t3NhsdxOTGf4I7zBMABaDdrYWyhrG661bFK7CdqoYKNJNJHBKKzsQW2mD1M6uKHTatGllf6gqqpTq0xoUtOFkzdvG4SfvnmbFnWNz0D927mgSG7HpzC8Y/F1q1t4uXMrLgXtPb9n4XHJuNhaVqsOAbhbRp7OnAyMMAg/HRhJi7qupaY9FxLN7YRUhrc33C/Gy8WOTXOHs+GlYfpXj6a1aVffgw0vDcPd7hHd0+M/TK0yoUkdd05eDzUIP30tlBb1jc/Oys7NR1lk4Jby7v1gSSN5q7X8fPIDAjBt09Yg2LRNW/Ku/oOlEBUKFOr77PnyIHGqg7x8sq8GYdWllUGwVZdWZJ0veUCI7eAe1Hj/ZSLmriL90NkS41VHurx80i+FYN/DcJChfQ9f0s7efKhjez43hFovj+Tq+OWkXzR+z1BVZWVkERkWqX+FBYSTEJNA2+5t9HFUahUtOvpy9dw1o8e4FXyLpx+bzjP9ZulfJ/ae5MKJizzTbxaxkYX7fr2wfA7dBnRl7thXib5dNQduVWQb/29NxvQgKz6VsAMXSjxe03E9iL0UQsL1WyXGEaI6qRZPIxISEhg9ejRTp07F19cXGxsbzp07x8qVKxk6dChQsAzPgQMH6NKlC2ZmZjg4GF+n7/HHH6dTp04MGzaM999/n0aNGhEZGcnOnTsZNmwYbdu2xdvbm82bN/PEE0+gUChYtGhRqaP9y4uNjQ2TJ09m/vz5ODo64urqyltvvYVSqXygpQaKevrpp1m2bBkLFixg3LhxxWZRFGVmZqbfd+Fv/3bpoJtrdtHhs2dJvBhKvF8g9Sf2xtLTieDvC0aJ+CwYi6W7A6df+AqA4O8P0GBqH1oumUDwT3/h3KYBXuN7cuqeHtmAb3bTe8siGs8eTMQePzz7tcGtWzMODF2mj6OyNMPaq7DhbFXbBftmdchNTiczIgGFiZIu//ciDj51OfrUByiUSv2eBLnJ6Wjz7t/RVNHOfrubzrOHkBQWQ2JoNJ3nDCEvO5drf5zQxxn84UzSopM4vPI3oOAH07lBwSZ8JqYqrN0dcW1am7yMHP3MgB7zxxB86CJpUQmYWpnTZEgnandswoanVlb8ST6ki9/upu2cIaSExZAcGk3bOUPIz84lYGthGT3+0UwyopM4+f5vd9PsYcSmN2n97GBC9vpRr28banZtxuYRhaMtQvf70/b5oaRFJJAYcAeX5nVpOX0A10rZiKgqivm/bXh98iIZF4NJ97uJy8Q+mHo6E/vDHgBqvj4RtYcjoS9+qk9j0awuACZW5qgdbbFoVhddbj7ZgXfQ5eSRddPwxkiTWtBhWzS8Oghes5M2nz1H8sUQEs8FUndibyw9nQm9e71qumAs5h6OnH/+SwDCvj9Aval9ab5kImE/HcSxbQPqjO/JuWc/0x+zwfNDSL4YQkZYLEpTFW6PtaTW6G5cfK3qzsIpzaQuTVm46TjNPJ3wre3C72cDiErJYNTdh2uf7jlPbGoWy0d3MUi31S8In1rOeLsV/43Oy9cQHFsw0idfoyU2NZMbkYlYmqmo/YiMyp3UuTELN5+kmacjvrWc+f1cEFEpmYxqV7DZ2af7LhCbmsnykYb7Bmw9H4xPTSe83ewrIdcVK//8fkz7PY02JhxtVAgqn24obBzJv1Swtqu6yzAUVvbk7l0HgObmGXQdBmLaZzJ5p7ajsLBG3W0kmqvHH4mlgyb1bMnCn/bTrJYLvnXd+f3kNaKS0hjVpWCQxqfbTxKbksHyiY8bpNt6+ho+ddzw9nAqdswxXZrz69HLrNxylPHdfAmPS+bbfX6M7/7PZoJWFZO6NWfhhsM0q+mCb21Xfj99g6jkdEZ1bAzAp7vOEpuSyfJxPQzSbT17E5/aLni7G458NlOrioXZmBfcDxcNfxRlZmZx607hspERkTHcCAjGztYGD/fSO1qqs0l92rPw2+00q+OBb31Pfj9ygajEVEb1KHig++nmQ8QmpbF82hMAdPf15u0fdvHbofN0buZFXEoGq37dR3MvD1ztC5aGzcvXEHx3YER+vobYpHRu3IrB0lxNbdfqV5cyN/2G7esLyQ+4Sd61q1gMGozS1ZWs7dsAsJo2HaWzC2nvvwuAxdBhaGJj0dwqGLGtbu6L5eixZG3drD+m5fgJ5AXcRBMZgUKlxrRDR8z79CPtkw8r/gTLQcLaLXiueoWsK4Fk+d/Afmx/1B4uJP2yEwCXV6agcnMi6tX/AXc7BFa+QszyNWRduIHJ3b0HdNk5aNPvv5dMdRC5ZjsNPnue9IshpJ27ifvEPph5OhP9/V4A6ix4ElMPJwKfL7yPtrq3HeJkh1Wzumjz8skKKNgI3HP2UGq/Oo6A5z4m53acfka0JiMbbeb997uqijZ9u4UJc8ZzJzSCO6ERTHx+PNlZOezfelAf542PXyUuOp5v3ltLXk5esX0B0u+2x+4Nf+md53lsWG/enPYWmemZONydyZKRlkFutuHKGFVNebXxAVAoaDymOzc2HUWnMf7sT21tgfeg9hx7++dyO0dxf7rqMx6zWqgWnQLW1tZ06NCBjz76iODgYPLy8qhVqxbTp09nwYKC5Sr+97//MXfuXP7v//4PT0/PEkf2KxQKdu7cycKFC5k6dSpxcXG4u7vTvXt3/R4AH330EVOnTqVz5844Ozvz2muvkZr6z5ekKQsffvghs2bNYvDgwdja2vLqq69y+/ZtzM3/+RqntWvX5vHHH2fv3r0VssHwvW5vO4WZgzXN5g7H3NWelJt3ODpxFZl3Cm6ULVztsfQsbLhm3I7jyMRVtFo6Ee8pfciKScJ/0ffc+bNwtETCuUBOzlqNz+ujaf7qaDLCYzgx6zMS71lux6FFPXpvflP/vtXSSQCEbjjCmZfWYOHhiGf/gh74fgdWGOT54IjlxJ2sest6nPpqBypzU/otn4K5rSWRF4L5deL75GYU3vDY1nA2GNFu4+bAtF3v6t93nDmIjjMHEX7yOj+PewcAKxdbnvhoFtau9uSkZRJ74zYbnlpJ2LHqtxHq+S8LyqjH8imY2VkScyGYPya8T949ZWTj6WywFFe0XyB7Zq+m4/zRdJg3ipTwGPY8t9pg2uKRRd/TYd4oerwzBUtnWzJikrjy00HOfrylQs/vYSVuO46Jgw01Xh6D2tWBrJu3CJi0nNyIghEkajcHTGsYjvhuvvcj/b+tWnjjNKIHObdjudRxZoXmvSJE/HEKUwdrGs8dgZmrPWk37nBywkqy7l6vzN0Mr1eZt+I4OWElPksn4fV0H7Jjkrj05noi77leqSzNaPHeVCw8HNFk55IWFInfnC+I+ONUhZ9fWejnW5fkzBzW/HWJ+LQsvN3sWf1Ub2o4FCyXEJeWRVSRZRjSsnM5cPUW8wcZX7M8Ni2LcZ8Xbj74/bFrfH/sGm283Pj2mb7ldzIVqJ9PHZKzclhz6EpBubnasXpiT2rYF3TSF5SbYaM/LTuXA9duM39AG2OHfORoAs6RZ26FuuMgFJZ26BIiyfljNbq0gunzCis7FLb3PEzLyyFn88eY9hyH+fgF6LLT0QT4kXfij0o6g7LVr3UDkjOzWbPnHPGpGXh7OLF65hPUcCzoKItLzSSqyF4uaVk5HLgYwvwRxjfzdnew4ctZQ/hg6zFGr/wVVzsrnuzhy9OPtS738ykP/VrWKyij/f7Ep2bi7e7A6ql9qeFQ8GA2LjWLqGTDWaBpWbkcuBzG/CEdKyPLVdqVG4FMff41/fuVn30NwNABj/POm69UVrbKXb92TUlOz2LNjuPEp6TjXcOF1S+MoYZTwWChuOR0ohIL24NDu/iSmZ3Lrwf9+HDjAWwszGnXuA4vjuyljxObnMa4tws7/7/fe5rv956mTcPafDt/QsWdXBnJOfQX6bZ2WE16CqWjE/lhoaS88Rra2IIBRkonJ0xc7+k4UiixnjYdE3cPdBoNmqhIMr75mqwd2wqjmJtj88LLmLi4oMvJQXP7FqkrlpNz6K+KPr1ykbbzCDH2NjjPfhKVqyM5AWHcmv4W+ZEFS9upXB1Q33PPbT9uAAq1Cvels3FfOlsfnrx5H1GvfVTs+NVR/B8nUDnYUGvuKExdHci8cYtrE94l5+59ttrNATNPwxmULQ98oP+3dYv6uIzsRvbtWPzaPQeA+5R+KM3UNP52vkG6Wx/8xu0PfivnMyofv36xATNzU15653ls7Gy4fuEG8ye8TlZG4XJ5rp6uBrP+HsTQyUMA+HjT/wzC33t5FXs27n34jJej8mrjA9Tq1gzbms5cL2WwX8MhHUGhIPCPk2V/ckJUEoXu3yxQLypNRkYGnp6e/O9//2PatGkV/vkbPKrfDWxFCzGVrsv7sZHdYe6rgzbj/pH+4yI0pa8XLaDf6qaVnYWq7QGWHfyv00VV3Y3mqwpFo3++tOJ/Tm71HKlZkVRdRlV2Fqq8/GtHKjsLVV7a0u8qOwtVXsItWQLsfhLTSt+3RsCbqkds8+dyMFLhVtlZqPLm3P6xsrNQ7XxRa2KlffZzj+D/V7WYKfBf5u/vz40bN2jfvj0pKSksW1awNM7fyyYJIYQQQgghhBBCCCGEEA9KOgWqgQ8++ICbN29iampKmzZtOHr0qH5TZCGEEEIIIYQQQgghhBDiQUmnQBXXqlUr/Pz8KjsbQgghhBBCCCGEEEIIUSlkJeqypazsDAghhBBCCCGEEEIIIYQQomLITAEhhBBCCCGEEEIIIYQQVZausjPwiJGZAkIIIYQQQgghhBBCCCHEf4TMFBBCCCGEEEIIIYQQQghRZWkVlZ2DR4vMFBBCCCGEEEIIIYQQQggh/iOkU0AIIYQQQgghhBBCCCGE+I+Q5YOEEEIIIYQQQgghhBBCVFnays7AI0ZmCgghhBBCCCGEEEIIIYQQ/xEyU0AIIYQQQgghhBBCCCFElSUzBcqWzBQQQgghhBBCCCGEEEIIIf4jZKaAEGXMpLIzIB4JGp2isrMgHgVaGUshHpJCrkX3pZQxNuLh5V87UtlZqPJUTbtXdhaqge8qOwNVntJEV9lZqPIUCimj+9EhZXQ/cnckRNUnnQJCCCGEEEIIIYQQQgghqizpjitb0nknhBBCCCGEEEIIIYQQQvxHyEwBIYQQQgghhBBCCCGEEFWWVlY2LVMyU0AIIYQQQgghhBBCCCGE+I+QmQJCCCGEEEIIIYQQQgghqixtZWfgESMzBYQQQgghhBBCCCGEEEKI/wjpFBBCCCGEEEIIIYQQQggh/iNk+SAhhBBCCCGEEEIIIYQQVZausjPwiJGZAkIIIYQQQgghhBBCCCHEf4TMFBBCCCGEEEIIIYQQQghRZWllrkCZ+k/OFFi3bh329vaVnY2HFhYWhkKh4MKFC5WdFSGEEEIIIYQQQgghhBDVwD+eKRAbG8uiRYvYtWsXMTExODg40KJFC5YsWUKnTp1QKBRs2bKFYcOGlUN2y8bYsWMZOHBgmR+3Z8+eHD58mBUrVvD6668b/G3gwIHs2rWLt956iyVLlpTJ59WqVYuoqCicnZ3L5HjlodkrI6g/sTdqOysS/YPwe2MdqQERpaapOagdzV8djXUdV9LDY7n83m9E7DpnEMd78uM0em4QFq72pARE4L/4B+JP3/xHn11vYi/qDO+Mg48XahsLNjeaTl5qpsExHHzq4rtwHI4t66HTaLmz8ywX3vqR/MychyyZh9PlpRG0eLIXZnZWRPkHs2/ROhICSy5XpwaedH1lJO7NvbCr5cKBpT/gt3aPQZwOzz1Bw/7tcKrvQV52LpF+gRx+bwOJIVHlfToPrf3LI2g2oaA8YvyDOfzmOhLvU8/qD2hHh3mjsKvjSkp4LKdWbSRkd2E9e+rER9jWcimW7tL6fRx5c73+vYN3DTovGEeNDo1RKBUkBkSw+9nPSI9MKLsTLAduk/vj8exQTF0dyAy4TfjitaSduW40rtrVgTpvTcbKtz7mXh5Ef7uT8LfWGsRxGNABzxdGYl7XA4XahOzQKKK+2kb874cr4nQemteUx/F+bjDmrvak3Yzg8uLvSShyTbmXU6fG+CyZhE0jT7Jjkgn8fDth3x/Q/73OhF7UGt0N28a1AEi+FMq1FRtI9g/Wx1GYKGk8byQ1R3bB3MWe7Nhkbm04zM2PtoKu6o+A2HA6gPXHrhGfnkV9V3vmD2hD67quRuMu2nyS7f4hxcLrudix+YXBAATFJPPlwUtci0wkKjmDeQPaMLFz43I9h8qw4UwA649dLyg3F7v7l9uF0GLh9Vzs2Pz8oPLOaoVQ+fZA1boPCis7dAmR5B7ZiDYyqOQEJirU7Qdh0rg9CktbdOnJ5J3dhebaCQAUjh6oOz2B0rUOSlsncg//Rv6FgxV0NuVjw9FLrD/gT3xqBvXdHZk/shut63sajbvox31sP3OjWHg9d0c2L5gAwLRPN+MXVPw3smvTOqyeNaRsM19BNpy4xvrDl4lPy6K+mz3zh3SktZe70biLNhxhu19gsfB6bvZsfmVksfDdF4J5/edD9GxWm48n9ynzvFeUDX/5sX7PaeJT0qlfw4X5Yx+ndcNaJcb/89QV1u85za3YRKwtzOjcrB5zR/fG3toSgKCIOL7cdpRr4dFEJaQwb+xjTHy8fUWdTqU6d+Ey3/28iWs3gohLSOSTFYt4rHvnys5WhbAYMgzLMeNQOjmSHxZG+herybt8yWhcdXMfrKbPRFW7NgozczQx0WTt2E7W7xv1ccy6dsPyyYmYeHqiMFGRH3GHrI2/kb1/b0WdUpmzHz8Ih2mjULk4khsUTuy7a8jyu2o0rnWfztiPG4RZk/ooTNXkBoUTv/pHMo+d18cx9a6N8wuTMG/WALWnG7HvriHp+60VdDblw31yv/9n776joygeAI5/L3eX5NJ7IQQChE4C0gSkiPRepHdBQGkqShNFQQRBfjZQwIoiqIiASO+I9NBLICEV0nu9u1z7/RG4cMmFmqrzee/eI3uzszPD7t7OTqPKlIJ6SMSCH8g6XVw9xAm/98ZhF1gT65rexH23i8gFP5iE8RjZGY/BHbCpWw2A7MvhRC/dQPbFBzxPVALjZo6h94ie2DvZE3zhBp/N/4LIkKhH2veFvs+z4Kt3+GfPcd55+T3j9hFTh9O+R1uq+fuiVqm5FnSdtUu+4Xb4ndLKRolq8cZAGtxX7//7nXWkPaTeX7NHC1reV+8//fHvRNxX7x9VTL3/yo/7OXa33t/ijYH4922FXRUXdHk6kq5EcHr57yReDCuynyBUJo/dKPDiiy+i0Wj48ccfqVmzJgkJCRw8eJDU1NRHjkOj0SCXyx/30CVGoVCgUChKJW5fX19++OEHk0aB2NhYDh06hLe3d4keSyqV4uVlvsJTEdSb2pu6k3ty+vU1ZIfF0+D1/jz/2zx2tX0LbY7K7D6uzfxpvWY6V5dv5s7us1Tt0YI2a6dzsN8iUu++TPPt24omi0Zzft4PJJ0NwX/0C7TfMJs9HWaTG5PyyMeWKayIO3yZuMOXaTx/WJG0WHs60eG3edzeforz839EbqfgmUWjafn5K5yY+HkpldrDtXylN81f7sGut9aSFh5P6+n9GLphLt92nEVeMeUqV1iREZ3EzZ1neGHBKLNhfJ+tz4Wf9hN3KRwLmZT2swYzeP0cvu88B42yfBtBHqTpq71pMrEHB2auJT0inuYz+tFv41x+7jALTTHl4dXUn25fTeP0is2E7QmiVvfmdPtqGlsGfkDC3R/2Tb0XYCEtGEzlUrcq/X+ZR9iOM8ZtDtU9eHHLu1z/9Sin//cHeVm5OPv7oFNrSjfTT8m173NUX/gSEW9/Q9aZYDxHd6Pehne49Pxr5MUkFwlvYSlDk5JJzOd/4D2pt9k4denZxHz+B8pbdzBotDh3bk6tT6ehSc4g4+jFUs7R0/Hp14qARWO4NPd7Us6GUGN0J1pvnMPB9rNQxhRt3LGp5k7rDbOJ+vkwQdO+xLVFHRp/NJ68lExid54FwK1NA2K2neDK2VB0ag21p/bmuV/ncrDDbFTxaQDUntYHvzGdOf/aarJu3sGpcU2e+Wwymkwl4d/uKdMyeFx7r0Ty8e5zvN27BU2qubM5KJSp6w+zZXpvvJ1si4Sf3bMZr3VpYvxbpzcw5MuddGlUzbhNpdHh42xHl4bVWLH7XFlko8ztvRLFx7vP83bv5vnldvYWU38+wpZpvR693L7aRZeGxb/Iq0yktZshbz+YvMO/oI8NQxbQDqt+01D9vBBDVprZfSx7TERiY0/egfUY0pOQ2NiDRcG9WiK3xJCRjCb0PJbtB5dVVkrN3vMhfLzlGG8Pfp4mNb3ZfPwqU1f/xZa3R+LtYl8k/OwX2/Na34KXkzqdgSHLfqFLE3/jtk8m9ESj0xn/Ts9RMXTZL3R5pnbpZqaU7L0Yzsd/nebt/m1o4ufJ5tM3mPrdXra8+SLeznZFws/u24rXejY3/q3TGRjy2Va6BNQoEjY2LYtPdp6haQ3PUs1Dadt79jof/3aAt0d2o4l/VTYfvcDUL35jy8KJeLs6Fgl/IfQ2736/g7eGdqJDYG0S07NY/PMeFv64m0+n5jecqPI0+Lg50aVZPVZsOlDWWSpXSqWKuv416d+zK2/MX1zeySkzVs93xG7KNLK++BTN1asoevfBcekyUsePRZ+YWCS8QaVCuW0r2vAwDCoV8oAAHF5/E4NKhWrnXwDos7LI2fAzutvRoNFg2bo19rPnoE9PIy/obFln8anZ92iPx7zJJCz6EuX56zgO7UnVrz8govdktHFJRcIrmgeQe+ICyZ/+iC4rG8eBXaj61ftEDX0DdXB+ncTC2hrN7Xiy9vyDx9xJZZ2lEufatw1+i14ifN43ZJ29geforjTYMJ8LHV4vph4iR5OayZ0v/sB7ovl6iGObhiRv/YfMoJsY1BqqTOlHg18XcPH518mLf/T3VBXJ8ClDGTzxRT6a+TF3wu8wesZIVmxcxugOL6HMUT5wX08fD159dzKXThVtsGvSOpBtP/7JjUs3kUqlvDxnPB9vXMa4jhNQKc3XnSuKZ17tTeOJPTh0t97fbEY/+m6cy8YH1Ps9m/rT9atpnFmxmfA9QdTs3pyuX01j68APjC/0N/degOS+er9r3ar0LVTvT4+I49i7P5IZnYjU2pLGL/egz4Y5bGj3JqrUrNLNuGBCX94J+Jd5rOmD0tPT+eeff1i2bBkdO3akevXqtGzZknnz5tGrVy/8/PwAGDBgABKJxPj3+++/T5MmTfj++++pWbMmVlZWGAwGMjIymDRpEh4eHjg4OPDCCy9w6dIl4/HCwsLo168fnp6e2NnZ0aJFCw4cMH3o9PPzY/HixYwZMwY7OzuqV6/On3/+SVJSEv369cPOzo6AgACCggpaAgtPH3QvfevXr8fPzw9HR0eGDRtGVlbBxZ2VlcXIkSOxtbXF29ubTz/9lOeff57XX3/dJD29e/cmJSWF48ePmxyva9eueHiY9gRMS0tjzJgxODs7Y2NjQ48ePQgNze+5lJGRgUKhYM8e0xdDW7ZswdbWluzsbLPTB12/fp2ePXtiZ2eHp6cno0ePJjm54Md18+bNBAQEoFAocHV1pXPnzuTk5BTzP/506kzszvXPtxGzK4iMm3c4/doapApLqg8svidNnYk9SPj7KsErt5N1K47gldtJ+OcadSZ2N4apO7kHEb8cIXzjEbJCY7mw4GeUsSnUGtv5sY4d8s0ebqz6i5Rz5nsQVOnyDAatjnPz1pEVFkfqpXDOvb0O394tsfMrv8ph8wndObnqT0L3BJEccoddb65FZm1J/X7Fl2v85XCOLPmFG3+dKvaF9eaxy7m6+RgpoTEkBUez662vcazqhmeAXynlpGQ0ntCdoJV/Er4niNSbdzjwRn551OlffHk0frk7t49d5dyXf5EeFse5L//izvHrNH654DxTpWaRm5Rh/Ph1eob0yARiThX0Ymk1ezCRhy5xYsmvJF+LIjM6iahDF1GmZJZqnp+W96Q+JP1ykKSNB1DdiiHqve/Ji03Bc0w3s+HVd5KIWvA9yZuPoCs0muaezJPXSNtzGtWtGNRRCcR/t5Pc4CjsW9YvzayUiFqTexL1yxGiNh4hOzSWKwvWo4xJocZ995T7+Y3phPJOClcWrCc7NJaojUeI+uUI/q8WVFTOTf2SiHUHyLgWRfatWC68+Q1YSHBv18gYxqV5beL3BpFw4CK5t5OJ3XGGpCNXcG5c9MVURbP+xA0GNK3FwOb+1PRwZHbP5ng52PD7mRCz4e2tLXGzVxg/12JSyFTl0a9pTWOYRlVdmdm9Kd0D/ZDLpGWVlTKVX241GdjMn5rujszu2Sy/3M4W7bUMZsot9l651SrjlJcOWdPOaK8dR3ftOIa0eDR//44hOw1ZQAez4S2qN0BatTbqP1ehv30DQ1YK+oRI9HEFo1D0CVFo/tmCLiQIg05bVlkpNesPX2RAqwYMbNOQml4uzH6xPV7Odvz+zxWz4e0VVrg52Bo/124nkKlU0a9Vwb3Y0dbaJMypG7exlsvoel/DQWWy/thVBrSow8Bn61LT04nZfVvh5WTL76fM9zq1V1jiZm9j/Fy7k0SmUk2/FqaNIjq9nrd/OcqrXZri4+JQFlkpNev3n2FA28YMbNeEmt5uzB7WBS9nB34/esFs+MvhsVRxc2REpxb4uDvxTG1fBrV/hutRBaNHG9WowszBL9C9ZQPksv/WMnXtWrdgxqSxdHn+ufJOSpmyGTQE5e5dqHbtRBcdRfZXq9AnJqHo089seO2tUNSHD6KLikSfEI/6wH7UQWexDAg0htFcukje8WPooqPQxcWi3PIH2vBw5I0CyipbJcp53AAy/thHxua95IXfJmnpWjTxSTgNNz+6L2npWlK/24zqagiaqFiSP/2RvKhY7Do+awyjuhpC0sffkbXrKAZNxe549CiqTO5D4i+HSNx4EGVoDJELfkAdm4LX2OLrIZHvfk/S70fRZZmvh4RO/Zz4H/eSey0S5a0Ywt5aAxYSHNtVzvMIYNCEgfy8ciPHdv9DxM1Ilr6xHGuFNZ37v/DA/SwsLHhn5dv88L8fiYsuOuJ/9qh57Pl9H5EhUYQFh/PRzI/xqupJncCK3zEgcEJ3zt1X7z94t95f+xHq/efv1vvPf/kXMWbq/cqkDOOneqdnyIhMIPa+54jQbSe58881MqOTSAuJ4fiiDVg52OBav5q5wwpCpfFYjQJ2dnbY2dmxbds21OqiPYfPns1vzf/hhx+Ii4sz/g1w69YtNm3axB9//GF8id2rVy/i4+PZtWsX586do2nTpnTq1Mk46iA7O5uePXty4MABLly4QLdu3ejTpw/R0dEmx/3000957rnnuHDhAr169WL06NGMGTOGUaNGcf78efz9/RkzZgyGB0zHEBYWxrZt29ixYwc7duzg6NGjfPTRR8bvZ86cyfHjx9m+fTv79+/n2LFjnD9/vkg8lpaWjBw5kh9+KBjStm7dOsaPH18k7Lhx4wgKCmL79u2cPHkSg8FAz5490Wg0ODo60qtXLzZs2GCyz8aNG42NHYXFxcXRoUMHmjRpQlBQEHv27CEhIYEhQ4YYvx8+fDjjx48nODiYI0eOMHDgwAeWy5OyreaOwtOZ+KMFlVZ9npakkzdwbV78D45rc3/ij5q2aMcfuYxbizoAWMilOAfWMIkXIP7oFdzuxvukxy5MailHn6c1mcZDp8oDwK1l3UeOpyQ5+rpj5+FE5LGCvOnytNw+fQOfZiX7Q25lnz88XJVeOo1GJcGhmju2nk5E/236fx1z+gbeDygPr6b+JvsARB+9jFcx+1jIpdQd+BzBv903FY5Egt8LTUiPiKfvz7MZf+FLBm1/nxrdmj1VnkqbRC7DNrAW6UcvmWxPP3oR++YlN1WLQ9sArGtVIev09RKLszRI5FKcAmuQeMT0vpN49Aoud+87hbk0q01ioXtQ4pHLODWugaSYl9kyhRUWMhl56dnGbSmnb+LerhG2NfNHfDk0qIbLs3WJP3jxKXJU+jRaHcGxqbT2Nx391srfm0u3i/bwMmfb+TCerelFFaeiv2X/VhqtjuC4VFrXKlxuXlyKfsRyO3ev3IqOKqh0LKRYeFRDH2364lYXFYyFd02zu0hrNkafEIW8eVesJ3yE9ZiFyNu+CNLyG31amjRaHcG3E2ldz7TC2apeNS5FPNrUfttOXufZOr5UecBL7W2nrtOtWR0UVpWvHDVaHcExybSuYzqdUqvaPlyKLNpz2ZxtZ0N41r8KVZxNR16sPXARZ1trBpTTM19J0Wh1BEfF07qBaYNzq4Y1uBRmfrqIxrV8SEjL4tiVWxgMBlIyczhw/gbtAipnw5FQAmQyZHXqFOm9n3fuLPKGjYrZqVAU/rWRN2xI3uWLxYaRP9MUWVVf8oqZkqhCk8uwblibnOOm7whyj59H8UyDR4tDIsHCVoEu49/Z81gil2EXWIv0QqOI049ewr55yd1rLRSWSGRStGnZDw9cAXlX88bV05WzRwtGzmryNFw8dZmGzRs+cN8xb4wiPTWdXb8+2qhjO4f8Z8qs9Ip9zt2r998uVO+PPX2j2Do85I8UuP2Y9f46hev9ZsI0HNkRdUYOKdcfbTonoeQYyvHzb/RY3TpkMhnr1q1j4sSJrFmzhqZNm9KhQweGDRtGYGAg7u7583A5OTkVmdYmLy+P9evXG8McOnSIK1eukJiYiJWVFQArVqxg27ZtbN68mUmTJtG4cWMaN25sjGPx4sVs3bqV7du3M23aNOP2nj17MnnyZAAWLFjA6tWradGiBYMH5w8bnzNnDq1btyYhIaHY6Xb0ej3r1q3D3j6/QjB69GgOHjzIhx9+SFZWFj/++CMbN26kU6dOQH7DR5UqVczGNWHCBNq2bcvnn3/OuXPnyMjIoFevXiZrCYSGhrJ9+3aOHz9Omzb5LZsbNmzA19eXbdu2MXjwYEaOHMmYMWPIzc3FxsaGzMxMdu7cyR9//GH2uKtXr6Zp06YsWbLEuO3777/H19eXkJAQsrOz0Wq1DBw4kOrVqwMQEFA6refWHk4AqJIyTLarkjOwqVr8GgjW7k6okkx7WauSMrF2zx/abOlij4VMWjTepAxjmCc9dmEJ/1yjyfsjqftqL0K/3YPUxoqAefkNLApPp0eOpyTZ3s1bbqG85SZn4OBTsmtLvPDuSG6fuUlySMWdX9DG3QkAZbJpeSiTMrB/wP+1jbtT0X2SM7B1LzqEHqBmt+ZYOdhw4/e/C+Jwc8DSTkGzKb059fFmTiz5lWrPN6bn16+xdegSYk8VndO5IpC52CORSdEkp5ts1yRlIL97fj0pqb0NTc9/g8RSDjo9EW9/Tcbflx6+YzmyuntPURe6ptRJGVgVcz5YeziRmHS5SHgLuQxLF3vUielF9mnwzjCU8akk/X3VuC101V/IHWzo/M8KDDo9EqkF15duImbbyafPWClKy1Wj0xtwsbM22e5qZ01y1oOHMwMkZSk5HhrLkkH/rR6WxZabrYLk7Ie/4E3KUnL8VhxLBv075q2WKOyQWEgx5Jr+5huUmUhszb/AtnBww6KKP+g05O1YAwo7LDsOR2JtQ96B9WWR7DKVlqPMP2fuNtLf42qvILmY3pL3S8rI4XhwFEuKGQUGcCUqnltxKbw34sE9DyuqtBzV3evKdFrQ/DJ6hPtRZi7Hb95hyfDnTbZfiExg29mb/Pb6gJJMbrlIy87NLyMH08ZEV3tbkjPMd/xo4l+VJS/3Zc7aP8nTatHq9DzfuDZzhlfeNRWEp2Ph6IhEKkOfZjoViz4tDQsXlwfu6/rr71g4OoFUSs5P61Dt2mnyvcTWFtffNiORW4JeR9bnn6E5F2Q+sgpM6uyQ/yI6xXT6O21KOrZuzo8Uh/NLA7GwsSZr998PD1wJGeshhZ67NUnpWN6t15WE6vNHkRefSvqxSti4BLi4558vacmm51JachqePsXPWNCoeUN6DevBy10nP/Kxpix4hcunrxBxM/KJ0lpW7tX7cwvV4XOfsN5vU0w9r4aZev891Ts1oeuX05ApLMlJTOevkctQVdKGJ0G454nWFOjVqxfHjh3j5MmT7Nmzh+XLl/Ptt98ybty4YverXr26sUEA4Ny5c2RnZ+Pq6moSTqlUEhaWP7dXTk4OCxcuZMeOHcTGxqLValEqlUVGCgQGFgxB9PTMv0ne/7L73rbExMRiGwX8/PyMDQIA3t7eJN6dGzE8PByNRkPLlgWLZzk6OlK3rvnW7MDAQGrXrs3mzZs5fPgwo0ePLrKGQnBwMDKZjGefLRga6OrqSt26dQkOzu8116tXL2QyGdu3b2fYsGH88ccf2Nvb07VrV7PHPXfuHIcPHzY7iiAsLIyuXbvSqVMnAgIC6NatG127dmXQoEE4O5t/SFGr1UVGhGgMOuSSoj1hqw9sQ7PlE4x/Hxv9cf4/CjenSSQPX0Cz0PcSSdFtRf6WSMzE8wTHvk9mSAynX1tLk/dHEvj2UAw6PaHf7UWZmI5BVzYzmTXo34auSwpGmfzx0grATCulRFKi65J2/mAs7vV82TDog5KLtATU6d+G5z8qKI8d4+6Wh5n/68c8zQBJsaNmGgzrQNThS+QkpBeEtsg/5yL2nefS3fnfk69H4928No1GdaqwjQJGRa4hM9seky5byeUubyK1tcaxbSDV33sJdVQCmSfNL65WkRQ9h3hgd4Ai58q9e5CZMvSf2puq/dvwz8AP0N83fZdPv9ZUfbEtQa9+SdbNOzg2qk7AotGoEtK4venYk2WkDBW+6xoMIDF3Ly5k+/kw7K0teaF+1dJJWAVXpNwwPFq5XQjPL7d6/7JyM3fxFXft5T8QoN7zPeTlzx2r+ft3LHtNgsO/gq7yT6tgTuHTw2Aoeh6Zs/10MPYKK14IND/yAvJHEvh7uxJQveKuUfUozJbRIxTS9qDQ/OuqYXXjthxVHvN/OcKCF9vibGv9gL0rlyJlhKHYMgqLTWb5L/uZ1Oc52jSsSXJ6Np9uPsSHP+/h/XH/jkXOhRL0kOfHtNenI1HYIK/fALuJk9DFxKA+fLBg99xc0ia9jEShQN60KXavTkEXF4vm0sVSTngpMfO79igj8+17dcBt2ihipi5El5rx0PCVmbnn6JKqylaZ0g+3/m259uJ7GCr4Om/3dB7wAm9+9Ibx77lj5wNFy0kiKb6CorBVMP+LuXw8+xMy0h5tKtvXFk+nVv2aTB/4+hOluzTVLlTv33m33l84+xLJA54b73qcen/9YR2IPnyJ3Pvq/ffEnAjmt+7zUTjb0WBER7p+NY0/+r5f4acO/rcRawqUrCeaANLa2pouXbrQpUsXFixYwMsvv8x77733wEYBW1vT3il6vR5vb2+OHDlSJOy9+f5nzZrF3r17WbFiBf7+/igUCgYNGkReXp5J+PtfuN+rWJvbptcXf/oUfmkvkUiM4e/dMApX2h/04z5+/Hi+/PJLrl+/zpkzZ4p8X9y+BkPBywFLS0sGDRrExo0bGTZsGBs3bmTo0KHIipm3U6/X06dPH5YtW1bkO29vb6RSKfv37+fEiRPs27ePlStXMn/+fE6fPk2NGkXnsF66dCkLFy402faibSMG2wcWCRuz9zwp5wtWXrewzE+jtYcjqvt6zVq7OhTpwX8/VVI61h6mrbZWbg6okvNvtHmpWei1OuNoAGO8bgXx3jve4x7bnOitJ4jeegIrNwd0uWoMBqgzuSfZ0Y82JP1p3dp/ntgLBeUqvVuutu6O5NyXNxtXhyKt5k+q08Ix+Hduyi9DFpNdwRZmith/3rgQMBSUh427I7n3lYfCzQHlA/6vc5PSi/QOULg5kJtc9Afd3seVqm0bsXvSZybblalZ6DRaUkNjTLanhsZQpUXFnWpAm5qFQatD7m7aGCh3cyzSa+exGQyoI+MByL0WiaJ2VapMH1ihGwXUxntK4fuOI+pirilVYnqRe5CVmwN6jZa8Qr1F/F/tRd0Z/Tg+ZAmZwbdNvmu4YAShq7YT82f+yIDMG7dRVHWjzvR+FbpRwNnGCqmFhJRs0wW9UnNUuNo9+AWawWBg2/lwejWu8a9dN6A4Dyy3h7x4zC+3MHo1/vest2BQZmPQ65DYml57EoV9kdEDxn1yMjBkpxsbBAD0qfFIJBZI7J0xpJfNb3NZcbZV5J8zhdZySc1W4lpo9EBhBoOBbaev06tFvWLPGWWehr3nQ3m157Nmv68MnG2t88uo0KiA1GwlroVGDxRmMBjYdjaEXk39TcrodmoWsWnZvLZuv3Gb/u5ze7O537Nt1iB8XSvPGgPOdjb5ZVRoVEBqVi6uDuanIvt+9wka+1dlXLdWANSp6oHCSs5Ly39mav8OuP+Hpn4T8ukzMjDotFg4m44KsHB2Rp9mfmF4477x+c+GuohwLJydsR07zqRRAIMBXWz+87Q27BayatWxGT6SjErWKKBLy8Sg1SFzMy0jmasjupT0B+5r36M9XotfJ/b1JeSevFh6iSxn9+ohloWeo/PrIelPHX+VV/pSdcaLXBu6kNzgyjOty/F9Jwm+UNChTG6Z/27Kxd2F1MSC+riTqxOpSeavN5/qVfCu5s3SHwoWP7/Xie1g5F5GdxhH7H3rwsz4YBrPdW3NjBdnkhT3aNNYlqXI/ef57RHr/YVnUbhfcfV+pZl6v93dev+eQvX+e7RKNZmRCWRGJpBwIYwRf6+g/rAOnP/yr8fImSBULI+1pkBxGjRoYFysVi6Xo9PpHrpP06ZNiY+PRyaT4e/vb/Jxc8sf/nPs2DHGjRvHgAEDCAgIwMvLi8jIyJJI8mOpVasWcrnc5OV+ZmamcVFgc0aMGMGVK1do1KgRDRoUnUOwQYMGaLVaTp8+bdyWkpJCSEgI9esXLAY3cuRI9uzZw7Vr1zh8+DAjR44s9phNmzbl2rVr+Pn5FSnTe40yEomE5557joULF3LhwgUsLS3ZunWr2fjmzZtHRkaGyae/nfk57LQ5KrIjE4yfzJAYlAlpeLUvGLFhIZfi3roeKUHFl1tK0C0825tOaeTVIZDks/mLV+o1OtIuR+DV3nTuSs/2ASTfjTcnOumJjv0g6uRMtLlqqvVrhV6dR8J9U4CUprwcFelRCcZPSmgM2Ynp+LUtyL+FXIrvs/WIOfdkebtf50VjqNO9Ob8NX0LG7aSnjq+kaXJUZEQmGD+pITHkJKTj2860PHyerUfcA8oj/vwtk30AqrUPIN7MPvWHdECZnElkoXne9RodiZfCcappOj+4U01vsmIq3oPVPQaNlpzLYTi2b2yy3bF9Y7KCSnh0gwQsLCv2HNUGjY70yxG4dzC977h3aETqWfOL5qaeC8W9g+n54/F8IOmXIjBoC37//Kf0pu4bAzgxfBnplyKKxCNTWGLQmzYQG3R64wN8RSWXSalfxYWTYaZT3pwOi6Ox74OnMQuKTOR2ahYDmv07Fsp9HHKZlPreLpwMizfZfjosnsbVHqXcshnwL1lgGAC9Dn1iNBbVTBcjl1arb7Jw8P10cWFIbJ1AbmXcZuHsiUGvx5D14JdSlZFcJqW+rwcnb5o2KJ6+EU3jGt7F7JUv6FYMt5MyGNCq+Hms9124RZ5WR68K3JD9MHKZlPo+bpws1EB/OjSWxn4eD9w3KDye2ymZDGhpun5MDXdHNs8cwG+v9zd+OjSoRota3vz2en+8HCvXmh5ymZT61b04GWz6O3T6egSNa5kfeaTK02JRqDOUxd3fJsO/dlZd4YG0WrQhIVg2a26y2bJZczTXHqNeJJEgkT/k2fBRwlREGi2qa6HYtHnGZLNNm6YoLxS/xpZ9rw54LZ1J3FvLyTl6tthw/wYGjZbsy2E4FaqHOLUPJCvo5lPFXeXVflR9YxDXR3xAzqWwh+9QgShzlMRExho/kSFRpCSk0Lx9U2MYmVxGk1aBXAsy39kqOiyalzq9zMvdJhs/J/ad5MKJi7zcbTKJsQV1+9cWT6Ndj7a8MXQW8bfjzcZX3jQ5KuML+MzIBNLu1vurFqr3V3m2ntk6/D0JZur9vg+p90c94vpuEokEaQWv6wrCwzzWSIGUlBQGDx7M+PHjCQwMxN7enqCgIJYvX06/fv2A/Gl4Dh48yHPPPYeVlVWxU9N07tyZ1q1b079/f5YtW0bdunWJjY1l165d9O/fn+bNm+Pv78+WLVvo06cPEomEd99994G9/UuLvb09Y8eOZdasWbi4uODh4cF7772HhYVFsUP+nZ2diYuLKzIC4Z7atWvTr18/Jk6cyNq1a7G3t2fu3Ln4+PgYyxKgQ4cOeHp6MnLkSPz8/GjVqlWx6Zw6dSrffPMNw4cPZ9asWbi5uXHr1i1+/fVXvvnmG4KCgjh48CBdu3bFw8OD06dPk5SUZNIIcT8rKyvjeg/3mJs6qDgh3+yh/oy+ZEXEkx0eT/0Z/dAp84jacsIY5tkvXiE3Po0rS37L3+fbPbyw9V3qTe1NzN5z+HRrhme7hhzst8i4z821u3l25aukXoog+VwotUa9gI2PK2E/HXysY1u7O2Lt4YR9jfzppRzr+6LNVpEbk0ze3cV1/V/qQkpQKJocFV7tA2i8YDiXP/wNTebD5/ItLUHf7aHV1L6kRSaQFhFPq2l90aryCP6zIG89P5lMdnwafy/fBOT/YLrVzl+ET2opw97LBY8G1cjLUZMelQBAl8XjqN+3NVsnfkpejso4v746MxdtBR56eem7PTSf1peMyATSI+Jpfrc8QrYVlEfnTyeTE5/GyWWb7u6zl4Gb36Hpq70J33eOml2bUbVtQ7YMLDRdkkRCvSHtubH5mNkpoy6s3UW3L6cRe/oGMSeDqdYhkBqdn2HrkA9LNc9PK+7rv6j1xQxyLt8iK+gmnqO6YuXjRsJP+wDwnTcSSy9Xwl77wriPTUM/ACxsrZG7OmDT0A9DnhZlaP6aE1WmDSTnchiqyHgkljKcXmiK26DniZj3dZnn73GFrd1Fs5VTSL8UTmpQKH6jXsDGx42Iu/eUBm8PxdrbhfPTVwMQ+dNBao7vSqP3RxG54RAuzWtTffjzBL260hin/9Te1J89mHNTVpF7O8m4PoE2R4UuN39atvj956n7Wj+UMcl3pw/yw/+VnkT9cqRsC+AJjG5Tj/l/nKRhFVcCfd34I+gWcRm5DGqZv2jXF/sukJipZHGh+e+3nbtFQFVX/M2sy6LR6gi729NHq9OTmJnLjbhUbCzlVHO1LxK+Mhrdph7zt5ykoY+Labm1uFtu+y+SmJnL4hcLldv5sGLLrTLTnj+AZbeX0CdEoY8LRxbQDom9M9or+fO4ytv0R2LnRN6+dQDobp7F0LInll3GoDm1A4m1LfK2A9FdP1EwdZCFFImLd8G/7ZyQuFUFjRpDRsVr7H6Y0R2bMH/9fhr6ehBYw4s/TlwjLi2bQXc7B3yx/QSJGdksHm06teS2k9cJqO6JfxVXc9HeDXONjoE1cbJ9cI/6im50u0bM/+0oDau6E1jNgz9O3yAuPZtBreoB8MXusyRm5LJ4WAeT/badvUlANXf8vUx79VrJZUW22VvnPw8X3l5ZjO7Skvnf/UXD6t4E1vLhj78vEpeayaAO+S8vv9hyhMS0LBZP6ANA+0B/Pli/m01HztOmYQ2SMnL4+Nf9NKrhjYdT/v1Yo9URFpvfCUKr1ZGYls2N6ARsrOVU86ic5fSocnOVRN+JNf4dE5vAjZAwHB3s8fZ6cGNUZZa7eRMOc+ejDbmJ5vo1FL16Y+HhgfKv7QDYTpiIhZs7Wcvy17dT9OuPLjERXXR+j215o0BsBg9FuW2LMU6b4SPRhNxEFxuDRCbH8tlWWHfpRtbnn5R9BktA2rqteC97C9XVUFQXg3Ec0gO5tzvpv+4CwG3mOGQersTP/R+Q3yDg/dFbJC5Zg/LSDaR31x4wqNTos+/WN+UyrGrlLzgvkcuQebpiVa8m+lwlmuhHW3S+Iold+xe1V84g+1IYWedu4jmqi0k9pNrbI7H0cuHWjILn6nv1EOn99RCNFuXdte+qTOlHtdnDCZn6GerbScjvzj+vy1GhzzUdoVlZbP5uC6OmjeBORAwxETGMnD4ClVLFgW2HjGHmfTaH5PhkvvnoO/LUmiLrAmRn5o9gvn/76x/OoHP/F5g/YQHK7Fzj+gXZWTnkqUxn5KhoLn+3h2Z36/0ZEfE0vVvvD72v3t/pbr3/1N16/+Xv9tJ/8zs882pvIvado8bdev/WYur9N83U+2UKK5rN6EfkvnPkJKZj7WxPozGdsfVy5tbO0whlS1+x+89VOo/VKGBnZ8ezzz7Lp59+SlhYGBqNBl9fXyZOnMjbb78NwP/+9z9mzpzJN998g4+PT7E9+yUSCbt27WL+/PmMHz+epKQkvLy8aN++vXENgE8//ZTx48fTpk0b3NzcmDNnDpmZ5TNf1yeffMIrr7xC7969cXBwYPbs2dy+fRtr6+KH/N+bBqk4P/zwA6+99hq9e/cmLy+P9u3bs2vXriJTHw0fPpyPP/6YBQsWPDC+KlWqcPz4cebMmUO3bt1Qq9VUr16d7t27Y2FhgYODA3///TefffYZmZmZVK9enf/973/06NHjscriUd34cgdSa0uaLR2HpaMtKRfCODrsI7Q5BT/MNj6uJj1lU4JCOfnKKgLmDqbR7MHkRCVw4pWVpN43hc7t7aewcraj4cwBWHs4kXHzDsdGfUzuneTHOnatMZ1o9NaLxr87bcsv39OvrSVyU/4LCddnatHorReR2VqTdSuWoNnfE7X5n5IvrMdwZs0O5NaWdFk8DmsHG+IuhrFp1DLy7subQxU3k3K183Rm3O6CBahbTu5Fy8m9iD4ZzK/D8l9gPzO6MwDDN71jcrxdb67l6uaKO5XJ+dU7kFlb0mHxOKwcbUi4GMafI5ehua887H3cTKbsij8Xyt6pq2g1azDPvjWIjKgE9k5ZZTI1EYBvu4Y4VHUj+LejZo8dvieII29/T7OpfWm/aAxpYXHsnvw5ccX0MK8oUrYfR+ZsT9U3hiD3cCb3ZjQ3Rn1IXkz+CzNLD2esCi1cHbi/oHJm19gft4HtUd9O5MKzrwBgYWOF35KJWHm7olfloQyLIWz656RsP152GXtCMX+ewtLZjnozB2Ll4UTWjTucHLkc5d17irWnEzY+BS/WcqOTODlyOQELR1PjpS6oEtK4/M6PxO4s6N1Vc1wXpFZyWn73hsmxbqz4gxsr8heLv/z2j9SfM5jGH72ElasjqoQ0In86yI1PtlDRdQvwIz03j7VHrpCcpcTf04lVo5+nyt0pJZKyVcQVmqoiS5XHweu3mdWzubkoScxSMuyr3ca/fzoezE/Hg2nm58F3E/4di1t2C6hOulLN2iNX88vNw5FVo56nilN+z+OkLCVxGaaNzsZy69GsPJJcqnSh59Ao7JA/2wuJjQOGlFjUf67CkJU/VF5i64jE/r6Xixo16q2fY/n8MKyHzcOgys6P48R2YxCJrROKkQW/YxbNuiJv1hXdnRDUf1S+l0zdmtYhPUfF2r1nSM7Iwd/blVWv9KGKS/70NUmZOcQVmrYsS6nm4KUwZr3Yrth4oxLTuBAex+op/YoNU1l0a1KT9FwVaw9cIDkzF38vZ1aN70oV5/yX10mZSuLSC5dRHgevRDKrb/Gdbf5NurVoQHq2krU7jpOckY1/FXdWzRhCFdf8Buuk9GziUgvqWP2eCyRXlcevh87xye8HsVdY06JedV57saMxTGJ6FsM++N7490/7TvPTvtM0q1ON72YVP7L53+DqjVDGT59j/Hv5yvwOEP16dObDd94sr2SVOvWRw2Q7OGI7egwWLq5oIyPImDcHfWJ+ByMLV1ekHvc1ikgssJswEamXNwadDl1cLDnffo1yx333bGtr7Ge8gdTdHYNaje52NJlLF6M+criss1cisnb/jdTJHrepI5C6u5AXGsmdyQvQxuZPbydzd0FepaCMnIb2RCKX4fneNDzfm2bcnrF1P/Hz8n+zZB4u+G370vidy4RBuEwYRO6Zy9weU3AeVhYp208gd7an6szBWN6thwSPWoL6TvH1kCYH/mf8t11jf9wHtkd1O5HzLV8FwGtcdyys5NT7dpbJfrdX/Mbt/20q5RyVjl+++g0rayve+HAG9o72XL8YzKyRc1HmFEyX5+njgeExO832H9sXgM83mz4TffTGcvb8vu/pE16KLtyt97e/r97/V6F6v52Zev++qat4dtZgWr41iMyoBPZPWUWimXq/fTH1foNej3Mtb+p+/RoKZ3tU6dkkXgpn26DFpIXEFAkvCJWJxPAoq94IReTk5ODj48P//vc/JkyY8PAd/iV+8/53P+SXhChL0XT5MDZidZiHaq4rv9EolUW8/t+zAGRp6fZFvfJOQsUmHoEeyhBf+XohljVJvUYPD/RfpxK/aQ/l/O/tYV5SZA3al3cSKry0oS+VdxIqvLSYB6/JIkBKhiijh3lb+u9eDLokDJF4lXcSKrwpt38u7yRUOu/4jSi3Yy+O3Fhuxy4tT7TQ8H/RhQsXuHHjBi1btiQjI4NFi/Kns7l/qh9BEARBEARBEARBEARBEARBqMhEo8BjWLFiBTdv3sTS0pJmzZpx7Ngx46LIgiAIgiAIgiAIgiAIgiAIglDRiUaBR/TMM89w7ty58k6GIAiCIAiCIAiCIAiCIAjCf4qY/LVkWZR3AgRBEARBEARBEARBEARBEARBKBtipIAgCIIgCIIgCIIgCIIgCIJQYenLOwH/MmKkgCAIgiAIgiAIgiAIgiAIgiD8R4hGAUEQBEEQBEEQBEEQBEEQBKHC0mMot09pSUtLY/To0Tg6OuLo6Mjo0aNJT08vNrxGo2HOnDkEBARga2tLlSpVGDNmDLGxsY99bNEoIAiCIAiCIAiCIAiCIAiCIAhlaMSIEVy8eJE9e/awZ88eLl68yOjRo4sNn5uby/nz53n33Xc5f/48W7ZsISQkhL59+z72scWaAoIgCIIgCIIgCIIgCIIgCIJQRoKDg9mzZw+nTp3i2WefBeCbb76hdevW3Lx5k7p16xbZx9HRkf3795tsW7lyJS1btiQ6Oppq1ao98vFFo4AgCIIgCIIgCIIgCIIgCIJQYZXeJD4Pp1arUavVJtusrKywsrJ64jhPnjyJo6OjsUEAoFWrVjg6OnLixAmzjQLmZGRkIJFIcHJyeqzji+mDBEEQBEEQBEEQBEEQBEEQBMGMpUuXGuf9v/dZunTpU8UZHx+Ph4dHke0eHh7Ex8c/UhwqlYq5c+cyYsQIHBwcHuv4olFAEARBEARBEARBEARBEARBqLD05fiZN28eGRkZJp958+aZTef777+PRCJ54CcoKAgAiURSZH+DwWB2e2EajYZhw4ah1+v56quvHhq+MDF9kCAIgiAIgiAIgiAIgiAIgiCY8ThTBU2bNo1hw4Y9MIyfnx+XL18mISGhyHdJSUl4eno+cH+NRsOQIUOIiIjg0KFDjz1KAESjgCCUOHW5znJWOVyTqso7CRVeT6fs8k5ChZeTKC/vJFR4Z2dcKe8kVGgB7ZLKOwkV3t/HqpR3Eiq89u02l3cSKjwLa2l5J6HCU8dqyzsJlcAP5Z2ACs/5N1FGDxPf/LXyTkKFV7d5cnknocIbctG3vJNQ4Y3qKZ6zhf8uNzc33NzcHhqudevWZGRkcObMGVq2bAnA6dOnycjIoE2bNsXud69BIDQ0lMOHD+Pq6vpE6RTTBwmCIAiCIAiCIAiCIAiCIAgVlh5DuX1KQ/369enevTsTJ07k1KlTnDp1iokTJ9K7d2+TRYbr1avH1q1bAdBqtQwaNIigoCA2bNiATqcjPj6e+Ph48vLyHuv4olFAEARBEARBEARBEARBEARBEMrQhg0bCAgIoGvXrnTt2pXAwEDWr19vEubmzZtkZGQAcOfOHbZv386dO3do0qQJ3t7exs+JEyce69hi+iBBEARBEARBEARBEARBEAShwvo3Ttbt4uLCzz///MAwBkNBzv38/Ez+fhpipIAgCIIgCIIgCIIgCIIgCIIg/EeIkQKCIAiCIAiCIAiCIAiCIAhChaUv7wT8y4iRAoIgCIIgCIIgCIIgCIIgCILwHyEaBQRBEARBEARBEARBEARBEAThP0JMHyQIgiAIgiAIgiAIgiAIgiBUWIZ/5VLD5UeMFBAEQRAEQRAEQRAEQRAEQRCE/wgxUkAQBEEQBEEQBEEQBEEQBEGosMRCwyVLjBQQBEEQBEEQBEEQBEEQBEEQhP8IMVLgEcTHx/Phhx+yc+dOYmJi8PDwoEmTJrz++ut06tSpRI7x/PPP06RJEz777LMSie9Bjhw5QseOHUlLS8PJyalUjuE/tjN1p/RC4eFERkgMFxasJ/n0zWLDu7euR5P3R+FYxwdlQjo3vtpB2E8HTcJU7dWCRrMHY1fdg+yoRK58tImY3UGPdVyfns2pNboTLoE1sHKxZ2/nt0m/FmUSR/Pl4/Fs1whrT2e0uSpSzoZy6cNfyLoVVwIlUzLavz6QpiNewNrRlpgLt9jz7jqSQmOKDe9e24cObw7Cu1ENnHzd2btwPWe+31Ns+Oem9OWFOUM5/d1u9i36uTSyUOr6vz6E54d3wdbRlrCLoax/91tiQm8XG77DsM48N7ADVetWAyDySjibP95A+KVbxjDWttYMfHM4zbo+i4ObA1HXItiw8HsiLoeVen5KmsPQPjiOG4zU3QVNWBQpy1ajOn/VbFibTs/hMLQPVnVrIrGUkxcWRdpX61GeOGc2vG335/H8+G1yDp0g4bX3SzEXJcdnXFeqT+2DpYcTOTfvEPruj6SfvlFseKfW9am9cAy2dauSl5BG1KrtxPx0wCSM76Se+IztgrWPG5rUTBJ3nCbsw1/QqzX5xxzbBZ9xXVD4ugOQc/MOEf/7g5RDF0stn6XNe1xXfKf0M5Zj2IIfyCymHC09nKj5/ljsAmuiqOlFzLe7CV+wrmwTXMqsuvbDqt8wLJxc0d2JQPnDKrQ3rpgNK60XgM3ISVj4VENiZY0+KQH1/u2od26+L5AU6wEjsezQDQsXd3Sx0Sg3fI324pkyytHT8xvXGf8pvbH2cCLrZgxXFvxE6gOeD1xb16PR+6Oxr+uDKiGdW1/+ReR9zwfePVtQ57V+2Pp5IpFLyQmP59aaXdzZ/I8xTN23XqTeWy+axKtKTGdv4JSSz2ApEOfRw1l26otVryFIHF3Rx0Si/PkrdCHmy+h+0toNsZ3/Kfo7EWS/M9m4Xf58TyzbdkVa1Q8AXUQIqt+/Qxde/Lla0Sn69sdmyDAsXF3QRkaS/dUqNFcumw0rbxSA7cTJyKrln0e6hHiUO/5C+cfvxjBWbdthM2IUUh8fJFIZ2pg7KH/fhOrAvrLKUokTZVRygi5e4YeNm7l+4xZJKal8vvRdOrVvU97JKhOuo3riPnkgMg9nVCHRxC76htyz182Glbk74/3OBGwa1cKyRhWS1/1F3KJvi4RzG98X15E9kPu4o03NJGP3CeKX/4jh7jNlZWPVsz+KgcOwcHZBFx1Jzjer0F43f63JGgRgM3Yy0qr3ftfiUe35C9Wfv5uEk9jaYTP6ZSxbt0diZ4cuIZ7c775Ec+50WWSpRLR4YyANRnbEytGWhAth/P3OOtJCiq/jA9Ts0YKWbw3CsboHGVGJnP74dyL2FLwjGXXiUxzu1jXud+XH/Rx758ci2zssHU/DUS/wz/vrufzd3qfPVCmSd+iNVdfBSBxd0MdGodq0Bt0t8/XZ+0lrNcDmzRXoYyPJWVzwLChv3QXFuLeKhM+c2hu0lfNaE4SHEY0CDxEZGclzzz2Hk5MTy5cvJzAwEI1Gw969e5k6dSo3bhT/0qikGQwGdDodMlnF/m/z7duKJotGc37eDySdDcF/9Au03zCbPR1mkxuTUiS8ra877X+eRfiGw5ya9hXuLerQdOlLqFMyubPzLACuzfxpvWY6V5dv5s7us1Tt0YI2a6dzsN8iUi+EPfJxZTbWJJ8J4c5fp2nxv4lm0596OYKoLSfIuZOMlbMdDd8cSIdf57Kz5esY9OW/qEmbV3rT6uWebH9rDSnh8bSb3p+RG+bxVce3yMtRmd1HprAiLTqR4J2n6bJg1APj9w6syTMjOpJwPeqB4Sqynq/0p/uEPnzz1iriI2LpO30Qs35ewNwXpqMqpozqtWrIqe3/cOv8TTRqDT0n9+Ot9QuY3+V10hJSARi/bApV61Tj65lfkJaQSpsB7Zn983u8fV+YysC2Wwdc57xC8uKVqC5cw2FwL7xWf8jtfi+ji08qEl7RLADlyXOkff49uqxs7Pt3w2vVImJGzCDvhmmDiMzbA9e3JqI89/CXMRWFR7/W1PlgLDfnfkf6mZv4jOlM41/mcardTNRm7lnW1dxpsnEuMT8f4trUVTi1rEvdjyaQl5JJ0s78l2qeL7al1vzhBL+xhoyzIdjU9KbBF68CELrgJwDUcSmELd5IbkQCAN5D2xP44yzOdJ5Dzs07ZZT7kuPerw21Fr3ErbnfkHH2Jt6juxCwcT5B7d9AHZNcJLzESo4mJZPoz/+g6qTe5ZDi0iVv0xHFS9PI/eYztDevYNWlL3bzl5PxxlgMyYlFd1ApUe3Zii4qHNQqZPUCsJk0E4NaRd6BHQAohk3Asn0XctasQB8TjaxJC+xmfUDW/KnoIm8VjbOCqdKvFQGLxnBp7vekng3Bb3QnWm+cw6H2s1CaudZsqrnTasNson4+zLlpX+LSog6NPxqPOiWTuLvPB3np2YR8to2sW7Ho87R4dWnKM59NRp2cSdKRghcMmTduc2LwEuPfBn3lGHwszqOHkz/7PNajpqBc9wW60KtYduyN7aylZM0djyHFTBndo7BFMXku2mvnsXB0NvlKVr8xmpOHUIZeA00eVr2GYjt7OVnzJmBIK3o/q+isnu+I3ZRpZH3xKZqrV1H07oPj0mWkjh+LPrFoGRlUKpTbtqIND8OgUiEPCMDh9TcxqFSodv4FgD4ri5wNP6O7HQ0aDZatW2M/ew769DTygs6WdRafmiijkqVUqqjrX5P+PbvyxvzF5Z2cMuPYuy3eC14m9t015ARdx2Vkd2qse5+QLlPRxBZ9xpZYydGlZpDw5SbcJ/QzG6dTvw54zRnLnVlfkHM+GKsaPviueA2AuA+KNiBUdJZtO2L78jRy1nyK9vpVrLr3weH9ZaRPHYs+yfy1ptq5FV1k/rUmaxCA3dT8a029N/9aQybD4YP/oU9PI+ujBeiTk7Bw98CQm1vGuXtyz7zam8YTe3Bo5lrSI+JpNqMffTfOZWOHWWiKqb96NvWn61fTOLNiM+F7gqjZvTldv5rG1oEfkHgxv462ufcCJNKCCUJc61al7y/zCNtRtCNAjW7N8HymFtnxFb9eK2veAeshr6DauApd2DXk7XthM30x2e9PxJBW9FozsrZB8dIsdDcuIHFwLvK1QZlD9oIJphtFg0CFohcLDZcoMX3QQ0yZMgWJRMKZM2cYNGgQderUoWHDhsycOZNTp04BEB0dTb9+/bCzs8PBwYEhQ4aQkJBgjOP999+nSZMmrF+/Hj8/PxwdHRk2bBhZWVkAjBs3jqNHj/L5558jkUiQSCRERkZy5MgRJBIJe/fupXnz5lhZWXHs2DHCwsLo168fnp6e2NnZ0aJFCw4cMO2hqlarmT17Nr6+vlhZWVG7dm2+++47IiMj6dixIwDOzs5IJBLGjRtXomVWd3IPIn45QvjGI2SFxnJhwc8oY1OoNbaz2fC1xnQiNyaFCwt+Jis0lvCNR4j49Sh1X+llDFNnYg8S/r5K8MrtZN2KI3jldhL+uUadid0f67hRm//h+qdbif+7+Bbk8J8Pk3TqBrl3kkm7EsmVZb9j6+OGjZkW9vLQckJ3/lm1jRt7gkgKucOfb65Bbm1Jo37F976JuxzOwSW/cO2vU+jU2mLDyW2sGPD5FHbO+RZlRk5pJL9MdBvfm+1f/sG5vaeJCbnNN2+uxFJhRat+7YrdZ+3rn3Po571EX48kLiyG7+euwUIiocFzAQDIrSxp3r0Vvy39iZtnrpMYFc+2zzaRdCeRF0Z1K6uslQjHMS+StWUPWVv2oIm4TcryNWjjk3AY2sds+JTla8j44XfU10LQRseS9sUPaKJisHm+lWlACws8PppL2pfr0d6pOCNrHqbaK72I3XiI2A2HyA2NIfTdH1HHpFB1XFez4X3GdEF1J4XQd38kNzSG2A2HiP3lMNWnFJSfY7PaZJy9ScKW46huJ5F69DLxW0/g0LimMUzyvvOkHLyIMjwOZXgc4Ut/Q5ejwqFZ7VLPc2nwmdyb+F8OEb/xEMrQGMIXrEMdk4z3WPPlqL6dRNi7P5D4+99osypPpe1RWfceTN6hXeQd2ok+JhrlulXokxOx6mq+0q+LvIXm+CH0dyLRJ8WTd2w/mktnkdUPNIaxbN8V1ZYNaC+cRp8YR96+7WgunsWqz9CyytZT8Z/ck6hfjhC98QjZobFcXbAeZUwKfsU8H/iN6YTyTgpXF6wnOzSW6I1HiPrlCP6vFjQipZwIJm53ENmhseRGJRL+7R4yr0fj2rKuSVwGrQ51Uobxk5eSVap5LSniPHo4yx6DyDu6G83RXehjo1Ft+Ap9SiKWncz/pt2jGP8GmpMH0d0q2oNXuXopeQe3o48OQx93G+V3n4CFBFmDZ0orG6XKZtAQlLt3odq1E110FNlfrUKfmISij/nzSHsrFPXhg+iiItEnxKM+sB910FksAwrOI82li+QdP4YuOgpdXCzKLX+gDQ9H3iigrLJVokQZlax2rVswY9JYujz/XHknpUy5v9yftE37Sf1tH+qwO8Qt+hZNXDKuo3qYDa+5k0jswm9I33IYXTHPQjZN65ETFEz69qNo7iSSfewC6dv/xibAvzSzUmqs+w9BvX8X6n070d2JIvfbVeiSk7DuUczvWngoeX8fRBcdiT4xnrwj+8k7fxZ5w4JrzapzTyR29mR9OB9t8FX0SQlor19BF1l5RnMHTujOuZV/Er4niNSbdzj4xlpk1pbU7l98Hb/xy925fewq57/8i/SwOM5/+Rcxx6/T+OWCdySq1CyUSRnGT/VOz5ARmUDsqWCTuGy9nGn3wVj2z/gKvUZXavksKVadB6I5vhfN8T3o42+j3rQGfVoSlh0e3NFIMeo1NGcOowsPNh/AYMCQmWbyEYR/M9Eo8ACpqans2bOHqVOnYmtrW+R7JycnDAYD/fv3JzU1laNHj7J//37CwsIYOtS0YhUWFsa2bdvYsWMHO3bs4OjRo3z00UcAfP7557Ru3ZqJEycSFxdHXFwcvr6+xn1nz57N0qVLCQ4OJjAwkOzsbHr27MmBAwe4cOEC3bp1o0+fPkRHRxv3GTNmDL/++itffPEFwcHBrFmzBjs7O3x9ffnjjz8AuHnzJnFxcXz++eclVmYWcinOgTWIP2raSzj+6BXcmpt/0eXavHbR8Ecu49K4BhKZ9G4Yf+KPXi4Sxq1FnSc+7qOQKqyoMawD2VGJKGOL9mIsa06+7th7OBN+rCCfujwtUadvULUEXiT2+GAcoYcuEnH82lPHVV7cfT1x8nDm6rFLxm3aPC03T1+jdrO6D9jTlJXCEqlcSnZ6NgBSmQVSmRRNoWG6GlUetVvUK5nElwWZDKsGtck9cd5ks/LEOaybNHi0OCQSLGxt0GeYvlRzfmUkurQMsrYWPzVVRSORS7EPrEnqEdP7S+rRSzg2r2N2H8fmdUg9esk0/OFL2DeuabxnZZy5iX1gTRyeqQWAdXUP3Do9Q/KBC+YTYiHBs38bpDZWZAaFPGWuyp5ELsM+sCZpR0zLJe3oZRxaPPp1968hkyGtWRfNJdOeoJrLZ5HVbfhIUUj9/JHVbYT22n1lKpdj0OSZBsxTI6tX8V8wSeRSHANrmPTeB0g8egWXFuavNedmtUks9LuedOQyTvc9HxTm1rYhdv7epBSu7Nb0otvFL+l85jOarZmOTTWPp8hNGRHn0cNJZUj96qC9YjqdpPbqOWS1iy8jebtuWHh4o97606Mdx8oKpDIMOZWjMcmETIasTp0iPdPzzp1F3rDRo0XhXxt5w4bkXb5YbBj5M02RVfUlr5jpdio0UUZCCZDIZSga+ZN1zPRZL/vYBWya1X/ieHOCrmMTUAtF4/y6nqWvJ/Ydm5N5OOghe1ZAMhky/zpoLhT6XbtwFln9R7vWpDVrI6/fEM3Vi8Ztls8+h/bGNWxfeQPnn7biuOoHFINHgUXleN3lUM0dW08nbv9d8Myjz9MSe/oGXg+o43s29TfZByD66OVi97GQS6kz8DmCfztq+oVEQqfPXuHimp0Pna6oQpDKsKhWG+1106lstdfPIa1VfH1W3qYrFu7eqHc8YHpkKwV2S37C7qOfUUxdhIVvrZJKtVBCDOX4+Teq2PPQlLNbt25hMBioV6/4F34HDhzg8uXLREREGF/kr1+/noYNG3L27FlatGgBgF6vZ926ddjb2wMwevRoDh48yIcffoijoyOWlpbY2Njg5eVV5BiLFi2iS5cuxr9dXV1p3Lix8e/FixezdetWtm/fzrRp0wgJCWHTpk3s37+fzp3ze9/VrFnQO9XFxQUADw+PEl9TwNLFHguZFFVShsl2VVIG1u6OZvdRuDsSbya8hVyGlYs9qsR0rN2dUCVlFgqTaYzzSY77IP5jOxP47nDkttZkhsZwZOjSCtFibufhBEB2oXzmJGfg6OP2VHE37NMK70Y1+Lbvu08VT3lzdHcCIDMp3WR7ZlIGrlUffbTH4DmjSItP5frx/IqbKkdF6Lkb9J0xiNhbd8hIzqB137bUbFKbhIjK0yte6uyARCZFl2La60GXkobUtegQSnMcxw5CorAme+/fxm1WTRpgP7A7dwa9WqLpLW1yFwcsZFLyCl1T6qQMXO5eb4VZeTiSUih83t17ltzFnrzEdBK2nUDu6kCz7YtAAhZyGXd+2EfUyj9N9rOt70vznYuxsJKjy1Fx+aUV5FSGh/FC5C72SGRSNIWuu7ykdJzvXpP/JRJ7RyRSKfp00+vMkJ6GhZPLA/d1XPM7EgdHkEpRbVpH3qGdxu+0l85i3Xsw2uuX0CfEIgtoirzFc5Wi0mtVzO+0+gG/09YeTiQmmb48u/d8YOlijzoxHQCZvYJuF7/EwlKGQafn8rwfSLpvRGDa+Vucn76a7LB4rN0dqfNGf9rteJ9DHWajScsu2YyWIHEePdy9Mirck8+QkYbE0XwZWXj6YD10IjmLX4dHnEbKeuhE9GnJaK+ZX0unIrNwdEQilaFPM50OQp+WhoXLg88j119/x8LRCaRScn5ah2rXTpPvJba2uP62GYncEvQ6sj7/DM25yveiUpSRUBLuPWNrCz0LaZLSsXdzeuJ4M/46hszFkVq/L8ufVUAuI3n9LpJWb374zhWMxOHutZZueq09yu+a0w93rzULKcpf1qHeV3CtSb28sQh8BvWRA2QunIO0SlVsX3kdpFKUvxadN7+isbn7rJybbPqMlJuUgX3V4uv4Nu5OKAvto0zOwKaY56oa3Zpj5WDDjd//NtnedErv/Oen7yv2GgL3SOwc7v72p5tsN2Slm50SCMDCowpWA8aT+/Gbxf726+Nvo/pxBbqYSCTWNli+0B/b2Z+Q88Gr6BNjSzobglAhiEaBBzAY8tuCJBJJsWGCg4Px9fU16dnfoEEDnJycCA4ONjYK+Pn5GRsEALy9vUk0Mz+lOc2bNzf5Oycnh4ULF7Jjxw5iY2PRarUolUrjSIGLFy8ilUrp0KHDo2W0GGq1GrVabbJNY9Ahl5jvnWfCUKgd7QFlmB++cHBJ0XgKxSmRmDnO4x63GFFbjhP/9xUUns7UfaUnbb6ewcG+C40LhJaVRv3b0GtJwZx2v7z0sfmAEonxfH0SDt4udH1vDBtHf4Suki1Y1bpfO8YtKVgc8JPx+XNGFykPiZltxeg5uR+t+rblo2HvmYwM+PqNL5jw8VQ+P/MtOq2OqKvhnPrzGNUb1XxAbBXVk10rtj2ex/nV0SS89h761PT8XW0UeCydS9L7n6FPz3xwBBWUgcL3F0nR+4npDqYK3bOc2jTA7/UB3Jz7HRnnQ7Hx86LO4nGoE9KI/HSLcbfcW7GceWE2MkdbPHo/S4MvpnJ+wPuVsmEAzBTZw8rxX8/MdfaQ4shaMB2sFchqN0AxchK6+Bg0xw8BkPvDSmwmz8Lh85/AAPqEGNSHd2PV0fy0BBWSuSJ5UJkU+e0v+nygzVZxpNM8pLbWuLdrSKP3R5ETlUjKifzRAomHCnrJZ924Teq5UDqf+pRqQ9oTtnbXU2WnbIjz6KGK3Hswf2JJLFBMeRv1lnXo4x9t7RbLXkORt+pIzpI3QVO5npEe6iH357TXpyNR2CCv3wC7iZPQxcSgPlyw0LchN5e0SS8jUSiQN22K3atT0MXForl0sZQTXoZEGQmPrejv1tM8Cdm2aoTHtCHEvruG3Is3sfTzpsqCSWgTU0lc+dvTJbW8mLtnP6SUMudOR2Jtg6xuA2zGTkIXF0Pe33evNYkF+ox0cr5cAXo9urAQLFzcUAwcViEbBWr3b8PzH403/r1z3Ir8f5h7J/KQk6foLar49wL1h3Ug+vAlchPSjdvcA/wIHN+NTT3febTEVyhmT6SiJBYoJsxF/dd69InF17F0ETfQRRSsGaoMu4bt/C+Rd+yH+rfVJZBeQah4RKPAA9SuXRuJREJwcDD9+/c3G8ZgMJhtNCi8XS6Xm3wvkUjQP2LvpMJTF82aNYu9e/eyYsUK/P39USgUDBo0iLy8/KHgCoXikeJ9mKVLl7Jw4UKTbS/aNmKwfWAxe0BeahZ6rQ7rQj1srd0civQOvEeZlIG1h2lrtpWbA3qNFvXdHnyqpHSzYVTJmU983AfRZCnRZCnJjkgg5VwoA258TdUezYnedvKx43oaIfvPE3OhYC5EmWX+JWvn7kj23R6SALauDuQkP34+7/EOqIGduyMv7yhYCMxCJqX6s/VoMbYrS2qPrRCLLJtz4cBZwi6GGv+WW+Zfa44ezmTc11PHwc2RzOR0HqbHxL70nvoiy0cu5PYN08WWE6MTWDp0AZYKKxR2CjKS0pmyaiZJtx+tga8i0KVlYtDqkLqa9saRujgVGT1QmG23DrgvnEnCm4tRnioYGi339UZe1QuvlYsKAlvk3/9qXNjN7T7jK+waA5rUTPRaHVaFerNbujkUGT1wjzoxA6tC9yPLu/ese72Oa80ZQvzvfxO7If8lXE7wbaQ2VtRbMYnIz7Yan+ANGh3KyPw1aLIuhePQpBa+E3tyY9Y3JZnNUqdJzcKg1WFZ6B5s6eZI3lPcmyorQ1YGBp0OCycX7h9jJnF0Qp/x4MXb9InxAORFR2Dh6IJiyDjjy1xDZgY5H78Dcksk9g4YUpNRjJyEPrFiXl/3Uxt/pwtfO46oizlHVInpWBU6p+49H+Td38PfYCDn7nWUeS0K+9o+1Jnej5MnzM8Xq8tVkxl8G9uaRUdnViTiPHq4e2UkKbRQsMTB2fw8wAoFspr1kFavjfWYGXcDS5BYWOCwbh85y2eju37RGNyy52Cs+4wgZ9ks9LfDSzEnpUefkYFBp8XC2fR338LZGX3ag3/39fH555EuIhwLZ2dsx44zeeGNwYAuNv8FizbsFrJq1bEZPpKMSvbCW5SRUBLuPWPL3AstXO7miPYR6iDF8Zo5ivQth0n9bR8AqptRWCisqbp0GomrNlWqzheGTPPXmsTRuciouML0CXevtahwLJycsRk+ztgooE9LwaDVmvQA192JwsLFFWQy0Ba/pl55iNx/nt8uFtTxpXfr+DbujuTeV8dXuDmQ+4B3GblJ6UVGBSjcHFAmF+2kZefjStW2jdgz6TOT7d4t66Jwc2DMqYIppS1kUtq8O5LACd35uc0bj5O1MmHIzsz/7S80KkBi72j+t99agdSvLta+/lgPm3o3cP5vv/1Xu8j9fB66m5eK7mcwoIsMQerhUwq5EJ6UWGi4ZIlGgQdwcXGhW7dufPnll8yYMaPIy/n09HQaNGhAdHQ0t2/fNo4WuH79OhkZGdSv/+hzB1paWqLTPdr0NMeOHWPcuHEMGDAAgOzsbCIjI43fBwQEoNfrOXr0qHH6oMLHAh56vHnz5jFz5kyTbdvrTHrgPnqNjrTLEXi1b0TM7oKhsZ7tA4jZa37IdUpQKFW6NjXZ5tUhgNRLERi0urthbuHZPoCQr/fcFyaQ5LMhT3zcxyKRYGEpf3i4EpaXoyIvR2WyLSsxjRptA4i/lv/C2kKe//L+4Ee/PvFxIo5fY02XOSbb+q6YRHJYHCdW/1VhGwQgf1ofVU68ybb0xDQatQ0k+loEAFK5jLrPNmTTR+sfGFePSf3oO+1FVoz9gMgrxS9MladUk6dUY+NgS6P2Tdi09MHxVihaLerroShaNyX30HHjZkXrpuQcLr7Ry7bH87gvepPEOUtRHjtj8p0m4ja3B5jeG1ymj8PCRkHystVo45NKNg8lyKDRkXU5HJcOgSTtLpjf1KV9IEl7zQ/vzwgKwb1rM5NtLs8HknUp3HjPslBYFbluDHp9fg9fCcX3+pGAxLLy/TQbNFqyLofj3CGQlN0F54dTh0BS9px9wJ7/UlotuvCbyAKboznzj3GzPLA5eWePP2DHQiSAzLLodk0ehtRkkEqRt+pA3onDT5/mUmbQ6Mi4HIF7hwDi7vud9ujQiLg95n+n086F4lXo+cD9+UDS73s+MEsCFlbFX0cWljLsa1ch5fSNYsNUCOI8ejidFl1kCLJGzdCeKygTWaNmaM6bKSNlLlnzJphssuzUF1mDZ8hduRB9UsHzhGXPIVj3G0nO8rnoIirfWi9GWi3akBAsmzUn7/gx42bLZs1RH//nATsWIpEgkT/kWfhRwlREooyEEmDQaFFevYVd22fI3HvKuN2ubRMy959+4nglCisMhkKdCfX6/Ht7ZRuRqdWivRWC/Jnm5J0quNbkTZqjOf141xr3XUea61ex6tDJpDykVaqiT0mucA0CAJocFZpCdfychHSqtmtE8n11/CrP1uPk0uJHgyScv4Vvu0Zc/rbgHYlv+wDiz4UWCVt/SAeUyZlEHbxosv3mH8e584/peoK9f55NyB/HubHJdJqhCkOnRR8diqx+U7QXTxg3y+o3RXvJTH1WlUv2QtO6qmWHPkjrNUG59gP0yfFF97lL6lsTXUxkSaVcECqcyvfmoYx99dVXtGnThpYtW7Jo0SICAwPRarXs37+f1atXc/36dQIDAxk5ciSfffYZWq2WKVOm0KFDhyLT/jyIn58fp0+fJjIyEjs7O+O8/+b4+/uzZcsW+vTpg0Qi4d133zUZdeDn58fYsWMZP348X3zxBY0bNyYqKorExESGDBlC9erVkUgk7Nixg549e6JQKLCzsytyHCsrK6ysrEy2PcrUQTfX7ubZla+SeimC5HOh1Br1AjY+roT9lN+SH/D2UGy8nDk9Yw0AYT8dpPb4LjR5fyRhGw7j1qw2NYY/z6kpq4xxhny7hxe2vku9qb2J2XsOn27N8GzXkIP9Fj3ycQEsnWyx8XFD4ekEgH0tbyC/N6IqKQPbau5U69ea+KOXUadkofBypt60PuiUecQV+gEtL2e+20PbqX1JjYwnNSKettP6oVHlcfXPgh/Efp+8QlZ8GoeW5z9EWMiluNeuCuT3RLD3csazQXXyclSkRSWQl6MiKcR0GH1erhplWlaR7ZXB3u930HvqiyRExhEfEUefqS+Sp1Rz6s+Ch89J/5tOWkIqvy/fAORPGTRw5nDWvPYZyXeSjGsTqHJUqHPzH9oatW+CRAJxYbF4+nkx9O0xxIfHcOz3Q2Wex6eR8dMfeCydTd61EFSXruMwuBcybw+yNu0AwPm18cg8XEmanz9dlW2P5/H4cDYpy1ajvhRsXHtAr1ZjyM7FkKdBcyvS5Bj6rPxevIW3V0TRa3bScNU0Mi+FkREUis/oTlhVdSPmx/0A1Jo/HCsvF65P/xKAmJ/24zuhG7UXjibm50M4Nq9NlREvcPWVgh42yfvOUe2VXmRfjTROH1RzzlCS9wXB3caCWm8PI+XgRVSxKUjtrPHs3wbnNg25OGxJ2RdCCYhZu4O6K6eTdSmMzKAQvEd1xtrHjbif8nu2+b09AitvF25OL7i32zb0A0Bqa42lqwO2Df0waLTkVsL7TmGqHb9jO/1tdGE30YZcw6pzHyzcPMnbtx0A6xETsXBxI3fVUgCsuvVHn5yALiZ/KkBZvQCs+w5FtXurMU6pf30sXNzQRd7CwsUN6yHjQCJB/eeTNwqXpVtrd9Fs5RTSL4WTGhSK36gXUPi4EXn3d7r+20NReLtwfnr+EO3Inw5SY3xXGr4/iqgNh3BpXpvqw58n6NWVxjhrT+9L+qVwciITsbCU4dmpCb6D23FpzvfGMA3fG0H8vvMoY1KwcnWgzhsDkNkruL3pGBWdOI8eLm/3ZhSv5L+41926jmXHXli4epB38C8ArIZMwMLZDeXaZWAwoL8TabK/ITMdNHkm2y17DcX6xXHkfrUEfXK8cSSCQaUEtemLnMogd/MmHObORxtyE831ayh69cbCwwPlX/nnke2EiVi4uZO1LP/3R9GvP7rERHTR+S+n5I0CsRk8FOW2gunvbIaPRBNyE11sDBKZHMtnW2HdpRtZn39S9hksAaKMSlZurpLoOwVzcMfEJnAjJAxHB3u8vSrBQu9PKOnbbfh+MhPl5VByz9/AZUR35FXcSdmwGwCv2WOQe7py+81PjftYN6gBgIWNNTIXR6wb1MCQp0V96zYAWQfP4DahP8pr4eReCMHKzxvPmSPJPHDmkddFqUhU2zZhN3M+2tCbaG9cw6p7b6TuHmTtzr/WbMZMxMLVnexP8681q5790Sclortz91prEIj1gKGodhRca+rd21D0HojNxBmodvyBtEpVFINHodrxR9ln8Ald/m4Pzab1JSMygYyIeJpO64tWlUfotoI6fqdPJ5MTn8apZZvu7rOX/pvf4ZlXexOx7xw1ujajatuGbB34gWnkEgn1hrTn5uZjGHSm54w6PRt1uun6SnqNjtykdNLDK+4IQvWBLShemoUuKgRdeDDydj2xcPEg7+/8tSas+r+ExMkN1bqP83/7Y01nADBkpef/9t+33bL3SHThN9AnxhjXFLDwrYXyly/LMmvCQ1S+u17FJhoFHqJGjRqcP3+eDz/8kDfffJO4uDjc3d1p1qwZq1evRiKRsG3bNqZPn0779u2xsLCge/furFy58uGR3+ett95i7NixNGjQAKVSSURERLFhP/30U8aPH0+bNm1wc3Njzpw5ZGaaDhFbvXo1b7/9NlOmTCElJYVq1arx9ttvA+Dj48PChQuZO3cuL730EmPGjGHdunWPXTbFub39FFbOdjScOQBrDycybt7h2KiPyb2TDIDCwwkbH1dj+JzbSfw96mOeWTgK/3FdUCakceHdn7izs6B3aUpQKCdfWUXA3ME0mj2YnKgETryyktT7ptZ52HEBqnRtxrOfF8w/32btdACurviDa//bgk6twe3ZutSZ2B25oy3qpAySTt/gYN+FqFMqxlzpJ9bsQGZtSY/F41A42BJzMYwNoz4yGVHgUMXVpJeyvaczk3YXvGhsM7k3bSb3JvLkddYP+7BM018Wdq3ZhqW1JWM+mISNoy3hF0P5ePQiVPeVkYuPG/r7eta8MLo7cis509fMMolr62e/se2z/AcvG3sbBs8eibOXKzkZ2QTtPsXmFRvRPajHagWUs/coKU4OOL0yEpm7C3m3ooif8g7auPxpkGTuLsi8CypsDoN7IZHLcHtnOm7vTDduz/pzH0nvrCjz9Je0xD9PIne2p8bMF7HydCb7xm0ujfgI1d17h6WHE9b33bNU0UlcHPERtReNpepL3VAnpBEy/weSdhb0kI/8dAsYoObcoVh5uaBJySR53znClha8dLN0d6TBqqlYeTqjzcol+3o0F4ctIfXvK2WX+RKU9OcJZM52VJ85CEsPZ3Ju3ObqyCWo75WjpzNWhRZEb3awYJ0U+8a18HixHarbiZxpMbVM014aNCcOo7RzwHrQWCycXdDdjiB7yRz0yfnT3Fg4u2Lh5lmwg0SCYsQkLDy8QK9DFx9L7oavydv/V0EQS0sUwydg4VEFg0qJ5sIpclYuwZBbcRfLvV/sn6ewdLaj7syBWHk4kXXjDqdGLkd59xyx9nRCcd+1lhudxKmRy2m0cDQ1XuqCKiGNK+/8SNx9zwdSGysCPxqPwtsFnSqP7FuxnJv2FbF/FvTStPZ2pfnq6fmLE6dkknbuFsd6vWc8bkUmzqOH05w+gsTOAev+o5E4uaC/E0nOinkYUvJ/0yycXLFwfbyXkFad+iKRW2L72vsm21VbfkS99aeSSnqZUR85TLaDI7ajx2Dh4oo2MoKMeXPQJ949j1xdkXrcV0YSC+wmTETq5Y1Bp0MXF0vOt1+j3LG9IIi1NfYz3kDq7o5BrUZ3O5rMpYtRH6mEI04QZVTSrt4IZfz0glHIy1d+DUC/Hp358J03yytZpS5jxz/InBzwfG0YMncXVCFRRL60EE1M/qhZmYcLch93k33q7PrC+G+bwNo493+evDsJ3Gj7MgAJK3/DYDDg9eYo5F6uaFMyyTx4hvgVlWik8n3y/jlMjoMjimH515ouKoLMhXPQJ+VfaxIXVyzcC641iYUFNmMnIvXMv9b08bHk/vg16j0F15o+OYnMBW9h8/JUnFZ+jz4lGdVff6D8Y2OZ5+9JXVidX8dvv3gcVo42JFwM46+Ry0xGFNj5uJmsFxB/LpR9U1fx7KzBtHxrEJlRCeyfsorEi6Yj3n3bNcS+qhvBvx0ts/yUNm3QUVS29lj1GonE0QV9bBS5q97BkJr/2y9xdMHCxf0hsZiSKOxQjHotfwpCZS7627fIXfEW+sibpZEFQagQJIanWZ1U+M/5zXtkeSehwgsxM0JfMHVLUvl62ZW19+yzyjsJFV5EovPDA/3HySWiL8WDBLSruFNbVRR/H6tS3kmo8Nq3i314oP84C+uHjzT9r1PHVrwpLoTKx/m3H8o7CRVecPPXyjsJFZ5PQMXoDFeR/XbRt7yTUOGN6imesx/GYe3e8k5CpfOy36ByO/a3kZvL7dilxaK8EyAIgiAIgiAIgiAIgiAIgiAIQtkQjQKCIAiCIAiCIAiCIAiCIAiC8B8h1hQQBEEQBEEQBEEQBEEQBEEQKiwxOW7JEiMFBEEQBEEQBEEQBEEQBEEQBOE/QowUEARBEARBEARBEARBEARBECosA4byTsK/ihgpIAiCIAiCIAiCIAiCIAiCIAj/EaJRQBAEQRAEQRAEQRAEQRAEQRD+I8T0QYIgCIIgCIIgCIIgCIIgCEKFJRYaLllipIAgCIIgCIIgCIIgCIIgCIIg/EeIkQKCIAiCIAiCIAiCIAiCIAhChaU3iIWGS5IYKSAIgiAIgiAIgiAIgiAIgiAI/xFipIAgCIIgCIIgCIIgCIIgCIJQYYlxAiVLjBQQBEEQBEEQBEEQBEEQBEEQhP8IMVJAeCzS8k5AJaAr7wRUAl3zFOWdhAovLklS3kmo8M5Yy8s7CRVeZ21OeSehQtOmi74mD9OkSmJ5J6HCy44UT0cPI1eIp6OHSYuzLe8kVHgWUnHPfpj45q+VdxIqvPpBn5d3Eiq8k43mlHcSKrz9lhnlnYQKL3GPW3knocJ7v7wTIPzniUYBQRAEQRAEQRAEQRAEQRAEocLSiwmESpSYPkgQBEEQBEEQBEEQBEEQBEEQ/iPESAFBEARBEARBEARBEARBEAShwjKIkQIlSowUEARBEARBEARBEARBEARBEIT/CNEoIAiCIAiCIAiCIAiCIAiCIAj/EWL6IEEQBEEQBEEQBEEQBEEQBKHC0pd3Av5lxEgBQRAEQRAEQRAEQRAEQRAEQfiPECMFBEEQBEEQBEEQBEEQBEEQhApLLxYaLlFipIAgCIIgCIIgCIIgCIIgCIIg/EeIkQKCIAiCIAiCIAiCIAiCIAhChWUQIwVKlBgpIAiCIAiCIAiCIAiCIAiCIAj/Ef+5RoHIyEgkEgkXL14s76QY+fn58dlnnxn/lkgkbNu2DSia3iNHjiCRSEhPTy/zdAqCIAiCIAiCIAiCIAiCIAiVW6lNHzRu3Dh+/PHH/IPIZPj6+jJw4EAWLlyIra1taR22Qlm3bh0vvfSS8W9bW1vq1q3L/PnzGThwoHH72bNnH7lM2rRpQ1xcHI6OjiWe3rJWc2xn6k7phbWHE5khMVxasJ7k0zeLDe/Wuh6N3x+FQx0flAnphHy1g/CfDhq/d6jjQ4PZg3AOrIGtrzsXF6zn1jd7TOKoO70vPj2bY+9fBZ0qj5SgUK4s/pXssLhSy2dpeP71gTQb8QLWjrbEXLjFznfXkRQaU2x499o+dHxzEFUa1cDJ1509C9dz6vs9RcLZezrTZd4w/J9vjNzakpTweP6c/TVxVyNLMTdPL+DNgfiP7Iiloy0pF8I4+/Y6MkKKLw8A354taDx7EHbVPciOSuTiR79zZ0+Q8XuPZ+tSf0ovXAJqYOPlzNHxn3JnzzmTOFp9OolaQ9ubbEs+d4u9fd4vsbyVFq9x3fCZ0hdLD2dyb94mYsE6Mk8Hmw0r93CixvtjsQusiXVNb+K+3UXEgnUmYRR1q1Jt1jDsGtfE2teD8Hd/IO6bnWWQk9LV9vWBNBnREWtHW2IvhLHv3XUkP+Bac6vtQ7s3X8Tr7rV2YOF6zn6/1yTMM6M60XRUJxyrugOQHHqHfz7fSviRy6Wal5LmPrY73q/0R+7hjDLkNtHvfUf2meLOIWd8F4zDJrAW1jW8Sfh+J7ff+77YuF36tqXW6jdJ23OaWxM+Kq0slDrrPv2xGTwMC1cXtJGR5Kxeheaq+f9nWcMA7CZORupbDYmVNbqEeFQ7/0K55XdjGMu27bAZPgppFR8kUhm62Dvkbt6E+sC+sspSibMb3BfHMYORurmSFx5J2oqvUF+4ajas4oW22A/qg2XdWkjkcjThUaSv/QnVySCTcBI7W5ynjUfRsS1SB3u0sXGkfrIW1fEzZZGlEmc3qC/2o4YgdXNFEx5J2idfkXfxitmwio5tsXuxL/I6BWWU+c2PqE6ZlpHd8IHYvdgXqacH+owMlAf/Jv3LbyFPUxZZKnGK/v2wHT4MqYsr2sgIMleuQnPZfBnJAwKwf2USsmrVkFhbo4tPIHf7dnJ/32wMI/Pzw27CS8jr1EXq7UXmylUm3/9bOI/ohcvLLyLzcEEdGkXCh1+jDLpmNqx91zY4jeiFdf2aSCzlqEOjSP5iAzn/nC/jVJcep+G9cJ4wCJm7C3m3okhcshblOfPlYdelDU7DemFVvxYSSzl5t6JIXvUzufeVh6V/NdxmjMa6YW3kPp4kLllL2k/byig3pcN1VE/cJw9E5uGMKiSa2EXfkHv2utmwMndnvN+ZgE2jWljWqELyur+IW/RtkXBu4/viOrIHch93tKmZZOw+QfzyHzGoK+f96FEFXbzCDxs3c/3GLZJSUvl86bt0at+mvJNVLrzHdcV3Sj8sPZzIuXmHsAU/kHn6htmwlh5O1LxbL1HU9CLm292EF6qX/FsMfWM4XUd0w9bRjtALIXz97hpuh0QXG75V99a8OG0w3tW9kcplxEXE8uc32zi65bBJnMPeGGGyX1piGuObjym1fJQm8V7kv0df3gn4lynVkQLdu3cnLi6O8PBwFi9ezFdffcVbb71Vmod8oLy8vDI/poODA3FxccTFxXHhwgW6devGkCFDuHmz4OW3u7s7NjY2jxSfpaUlXl5eSCSS0kpymajatxVNFo0m+PM/OdB1Psmnb9B2w2wUPq5mw9v4utP251kkn77Bga7zufHFnzT5YAw+vVoYw0gVVuREJXLlw19RJqSZjce9dT3CfjjA4V7vcWzoR1hIpbT7dS5ShVWp5LM0PPdKb1q/3JNdC9bxTZ93yU7KYMyGeVjaWhe7j1xhRVp0IgeW/UpWovmysXawYcIf76HT6Ngwdjlfdp7N3g83oMrMLa2slIgGU3tTf1IPgub/yJ6eC1AmpfPCr3ORPaA83Jr503bNNCI2/8OuLm8Tsfkf2q2dhusztYxhZDZWpF+LJmj+jw88fuyhS/zReKrxc3j0xyWWt9Li1q8NNRaN485nW7jYZRaZp4NpsPFtLH3czIa3sJKjScnk9udbyLkWZTaMVGGFOjqBqMUbyCvm+qtsWr3Sm5Yv92Dfgh9Z12cBOUnpDNsw96HXWnp0EkeW/UZ2YrrZMFlxqRxZ9hvr+rzLuj7vEnniOoO+mYlbbZ9SyknJc+n7HNXeH0/sF5u51u1Nss5cp87P72JZxfw5JLGUoU3JJO6LzeRej3xg3JY+7vguGEvWKfMvYioLqw4dsXt1Grm/rCft1Ylorl7GcckyLNw9zO+gUqH8cyvpM2eQOmEMuRvXYztuAtY9+xiDGDKzyN34M+mvTSV18nhUe3dj/9Yc5M1bmI+zgrPp+jwub71KxncbiR3xCuoLV/BYuRSpl/kysm4agOr0ORKnzydu5BRUQRfx+OwD5HX9CwLJZHiuXo7U24vk2YuIGTiOlA8+RZeYXEa5KlmKLs/jNHMKmT9sJH7UZNQXr+D++VKknubLyOqZQFSnz5H8+tskjHkV9bmLuH2yGHmdgjKy6d4Jp6kTyfzmJ+KHvETqByuw6fI8TlNfLqtslSjrFzriMH0aOT/9TPLLL5N3+QrOy5dj4WG+jAwqJblbtpI6/TWSR48l+6f12L08AUWf3vdFaoUuNo6stV+jS0kpo5yULfue7fGcP4mU1b8R0W86yqBrVPt2ETJvd7PhbVo0Iuf4BW6/vICI/jPIPXUZ37XvYdWgZhmnvHTY92iPx7zJpK75lagB08gNukbVrz8otjwUzQPIPXGBmEkLiHpxOrmnL1H1q/exql/wLGlhbY3mdjxJ//sBbWJqWWWl1Dj2bov3gpdJXLWJ0J6vkXP2GjXWvY+8ivkykljJ0aVmkPDlJlTBEWbDOPXrgNecsSR8/is3O0/hzpyVOPVui9fssaWZlQpBqVRR178mb8+cUt5JKVfu/dpQa9FLRH/2B+e6zCbjdDABG+djVUy9RHK3XhL9+R/F1kv+DQa8+iJ9X+7PN++uZXbvmaQlpfH+hkVY2yqK3ScrPYvNKzcxd8As3ug2nUO/H2D6itdo0v4Zk3DRN6N4qdlo4+f1rtNKOzulQrwXEYSnV6qNAlZWVnh5eeHr68uIESMYOXIk27Zt4+eff6Z58+bY29vj5eXFiBEjSExMNO53b4qcnTt30rhxY6ytrXn22We5csW0x8+JEydo3749CoUCX19fZsyYQU5OjvF7Pz8/Fi9ezLhx43B0dGTixIlm03n9+nV69uyJnZ0dnp6ejB49muTkgsrj5s2bCQgIQKFQ4OrqSufOnY3HOXLkCC1btsTW1hYnJyeee+45oqIKfpwkEgleXl54eXlRu3ZtFi9ejIWFBZcvF/QULDx90IMUnj5o3bp1ODk5sXfvXurXr4+dnZ2xMeYerVbLjBkzcHJywtXVlTlz5jB27Fj69+//SMcsDXUm9yDilyNEbjxCVmgslxb8TG5sCrXGdjYbvtaYTuTGpHBpwc9khcYSufEIEb8epc4rvYxh0i6Fc+WDX7jz5yn0eVqz8fwzYjlRm/4mMySGjOvRnH1jLbZV3XBuXKNU8lkaWk3ozt+rthG8J4jEkDtsfXMNcmtLAvoV37Mk9nI4+5f8wtW/TqFTmy+btq/2ISMuhT9nfU3MpXDS7yQTcfwaadGJZsNXFPVe7s7VL/7k9u4gMm7e4eRra5EpLPEbUHx51JvYnfi/r3Jt1V9k3orj2qq/iP/nOvUmdjeGiT18mUvLN3N7d1Cx8QDo8jSokjKMn7z0nAeGrwiqTO5Dwi+HSNh4EGVoDBEL1qGOScF7bFez4dW3k4h49weSfj+KLsv8w1D2xTAiF60n+c/j6CtpT9PCWkzozolVfxKyJ4jkkDvseHMtcmtLGjzgWou7HM7hJb8Q/NcptMX0cLt18AJhhy+RGhFPakQ8f3/8O3m5Kqo09TcbviLynNiX5F8PkvzLAVS37nD7ve/Ji03BY0x3s+Hz7iQR/d53pGw+gu5BD9QWFtRc9QYxK35FHZ1QSqkvG4oXh6DaswvV7p3ooqPIWb0KXVISij79zIbXhoWiPnwQXVQk+oR41Af3k3fuLPKAQGMYzeWL5B0/hi46Cn1cLMqtf6AND0feMKCsslWiHEa+SPa2PWRv2402Ipq0FavRJSRiP6iP2fBpK1aT+eMm8q7fRHs7hvRV36OJjsGmfStjGLt+3bFwsCfpzQWoL11DF5eI+uJVNKHhZZWtEmU/YhA5f+4m589daCOjSf/kK3QJidgVU0bpn3xF1vrfjGWU8dV3aG/HoGjf2hjGMqAB6stXyd17CF1cAurT58jddxjL+nXLKlslymbIYJQ7d6HcuRNdVDRZK1ehT0rEpn8x11roLVQHD6GNjEQXH49q/37yzp7FMrDgWtPeuEnW6jWoDh2qtKMnHsZ1/ADSN+8j/fe95IXdJuHDr9HEJ+E8opfZ8Akffk3qN5tRXQlFExVL0ic/khcVi/0Lz5ZxykuH87gBZPyxj4zNe8kLv03S0rVo4pNwGm6+PJKWriX1u82oroagiYol+dP88rDrWFAeqqshJH38HVm7jmLQVP7zyP3l/qRt2k/qb/tQh90hbtG3aOKScR3Vw2x4zZ1EYhd+Q/qWw8U+P9o0rUdOUDDp24+iuZNI9rELpG//G5uAyvNM9KTatW7BjElj6fL8c+WdlHLlM7k38b8cIn7jIZShMYQvWIc6JvmB9ZKwd38g8fe/0RZzXv0b9J7Ql82rNnFqz0miQ6L5YuanWFlb0b5/h2L3uXbqKqf3nuLOrTvER8Wz4/u/iAyOpH6LBibhdFod6Unpxk9mamZpZ6dUiPci/00Gg6HcPv9GZbqmgEKhQKPRkJeXxwcffMClS5fYtm0bERERjBs3rkj4WbNmsWLFCs6ePYuHhwd9+/ZFc/eB6sqVK3Tr1o2BAwdy+fJlfvvtN/755x+mTTNt5fz4449p1KgR586d49133y1yjLi4ODp06ECTJk0ICgpiz549JCQkMGTIEOP3w4cPZ/z48QQHB3PkyBEGDhyIwWBAq9XSv39/OnTowOXLlzl58iSTJk0qthe/TqczTqnUtGnTpylKE7m5uaxYsYL169fz999/Ex0dbTIiY9myZWzYsIEffviB48ePk5mZaVyzoDxI5FKcAmuQcNS0kSfh6BVcm9c2u49L89pFwx+5jHPjGkhk0idOi9w+f4RGXlr2E8dRlpx93bH3cCbsWEFZ6PK0RJ6+gW8z82X3qOp2aUbs5QgGfzWDWee+YvKuD2k6rOPTJrlU2VVzR+HpRNx954Y+T0vCqRu4F3MuQf5IgbhC51PckcsP3Kc4nq3r8+LlL+lz7GOe/XgCVq4Ojx1HWZLIZdgF1iT9yCWT7elHL2HfonK+ECoNTr7u2Hk4EVHoWos+fYOqT3mt3U9iIaF+n1bIFVbEnA8tsXhLk0QuwzawFhlHL5pszzx6Edvm9Z4q7ipvDEGbkkHyrwcfHrgik8mQ1alD3rmzJpvzzp1F1rDRo0VRqzbyBg3RXL5YbBj5M02RVfVFc6VyTT0FgEyGZf06KAtNa6M8eQ6rxg2K2akQiQQLGxv0mVnGTTYdWqO+ch2XuTOouv93vDd9g8P44WBRCZfRksmwrFcH1WnTMlKdPodlYMNHi0MiQWKjQJ9RUOFXX7yKZb06WDbIv+dLfbyxbtMS5fFTJZb0MiOTIa9TF/VZ02tNffYslo0erYxktf2RN2xE3sVLDw/8byGXYd3Qv8jUPzn/XEDRtP6jxSGRYGGrQJee9fCwFZ1chnXD2uQcNy2P3OPnUTzzGPcjWwW6jH9BeZghkctQNPIn69gFk+3Zxy5g0+wRzxkzcoKuYxNQC0Xj/GcrS19P7Ds2J/PwgzvlCP8OErkM+8CapBWql6QdvYzDf7he4lnNExcPFy7+XXC9afO0XDt9lXrNHv1ZO+C5QHxq+XD9jOnoW+8aVfju7DrW/PMtM1fNwrOaZ4mlvayI9yKCUDJKbU2Bws6cOcPGjRvp1KkT48ePN26vWbMmX3zxBS1btiQ7Oxs7Ozvjd++99x5dunQB4Mcff6Rq1aps3bqVIUOG8PHHHzNixAhef/11AGrXrs0XX3xBhw4dWL16NdbW+UOGXnjhBZMX5JGRkSbpWr16NU2bNmXJkiXGbd9//z2+vr6EhISQnZ2NVqtl4MCBVK9eHYCAgPweeampqWRkZNC7d29q1cofKlq/vulDUUZGhjFPSqUSuVzO119/bQxfEjQaDWvWrDHGOW3aNBYtWmT8fuXKlcybN48BAwYAsGrVKnbt2vXQeNVqNWq12vRYBh1yyZO/hAewcrHHQiZFnZRherykDKzdza+VYO3uaDa8hVyGlYs9qmKm6XiYxu+PJPn0DTJv3nmi/cuanYcTADmFyiInOQPHYoZYPipnX3dajOrEyW93c+zLP/FpXIseC8egy9Nwacs/TxV3abG+Wx6qQuWhSsrAtmrx5WHt7oQqudA+ycWff8WJO3yJ6B1nyLmTjF01dwJnD6Lz7/PY3f3dYkerlDe5iz0SmRRNoTLTJGVg6e5UPomqgGxL8VoDcK9blTFb30dmJScvR8WWyZ+REhr71PGWBdndc0ibnG6yXZOcjsPdcnsSds3r4T68E9e6zHy6BFYAFo6OSKQy9Gmm00UY0tKwcHZ54L4uG3/HwtEJpFJy169Dtdt0bQ6JjS2uv24GuSXodWR/8Rma85Xv5YnUyRGJTIo+xXToti41Danrg8voHofRg5EorMnZd9S4TebjjXWLZ8jZfZDEGW8j8/XBZe4MJFIpGd/8XKJ5KG0Wd8tIl1qojFLSsH7EMrIfORiJtYLcAwVlpNx/mAxnRzy+/Ty/0UAmI3vzn2T9+GuJpr8sWDjePY/STMtIn5qGhcuDy8h98+9YODmCVEr2D+tQ7qz86+A8Kpmzg9n7uDY5DVs350eKw2XCQCQKazJ3HSuFFJYt6b3yKHQ/0qakP3J5OL80EAsba7J2/10aSSx3xjJKSjfZrklKx97N6YnjzfjrGDIXR2r9vgyJRIJELiN5/S6SVv/71vAQiiqol6SbbM9LSsf5P1wvcXLPv++kF7pHpyen4+5TzDSUd9nY2/DtmXXILeXodXq+fmc1l45dNH4feiGEz9/4lNjwGJzcnRg8fShLt3zMa52nklWJGnnFexFBKBml2iiwY8cO7Ozs0Gq1aDQa+vXrx8qVK7lw4QLvv/8+Fy9eJDU1Fb0+f6mI6OhoGjQo6I3RunXBUGcXFxfq1q1LcHD+Iobnzp3j1q1bbNiwwRjGYDCg1+uJiIgwvpxv3rz5A9N47tw5Dh8+bNIYcU9YWBhdu3alU6dOBAQE0K1bN7p27cqgQYNwdnbGxcWFcePG0a1bN7p06ULnzp0ZMmQI3t7exjjs7e05fz6/10lubi4HDhxg8uTJuLq60qeP+aHfj8vGxsakkcHb29s4HVNGRgYJCQm0bNnS+L1UKqVZs2bGci/O0qVLWbhwocm2wbaNGGIfWMwej6fI8BuJhAcNyCkyWufuiIwnHcbTZMk4HBtU40i/RQ8PXE4C+rehz5IJxr83vJQ/X32RHEskZgro8UgsLIi9Es7BjzcBEH8tCo86VWk+unOF+fHzG9CGlssLGhWPjF4BFM26RCIxU0imihbX45dh1PbTxn9n3LxDyqUI+p/5DJ9OTR467VC5K1JoT30KVWoN+7eh+5KCc2vTS3fPrULhHuXcehQp4XF832M+Vg421O3Rgt7/m8zPQxdXmoYBKOae/IQnkYWtNTVXvk7krNVo0ypPheShipQRDy2j9JnTkVjbIK/fANuXJ6GLjUF9uGDkhEGZS+orLyNRKLB8pim2r0xBFxf7wBEFFZmhcCE94nlk060jjpNHk/TGe+jT0gu+sLBAl5pOyuJPQa8nLzgUqbsrDmOGVLpGAaMnOI8AbLp2xGHSGJLfWmBSRlZNG+MwfiRpy74g72owMt8qOL05FYfkVDK/q6xlZO48evAuqdOnI1EokDdogP3kSehiYlAdPFR6aayIzJbbw88th94dcJ8+ktuvLkKXmvHQ8JVG0R+2R6pn2PfqgNu0UcRMXfjvKg+zTMtD8pD628PYtmqEx7QhxL67htyLN7H086bKgkloE1NJXPnb0yVVqDRK8pmyMmrfvwOvLJ1q/PvDcXffTxjMXG8PKRdltpKZ3V/D2taawOca89K7E4iPjufaqasAnD9yzhg2+mYUN8/dYPWxb+g46AW2f/tnCeWo5In3IsI9+pKojAtGpdoo0LFjR1avXo1cLqdKlSrI5XJycnLo2rUrXbt25eeff8bd3Z3o6Gi6dev2SAsB35uaR6/XM3nyZGbMmFEkTLVq1Yz/trW1fWB8er2ePn36sGzZsiLfeXt7I5VK2b9/PydOnGDfvn2sXLmS+fPnc/r0aWrUqMEPP/zAjBkz2LNnD7/99hvvvPMO+/fvp1Wr/PltLSws8PcvmBMxMDCQffv2sWzZshJrFJDL5SZ/m/uxKDyl0aM84M6bN4+ZM017bO6sM+kJU1lAnZqFXqsz9vK+x8rNochogHtUSRlYezgWCa/XaJ9o6p8mi8dQpWtTjgz4AGVcxV346+b+88RcCDP+LbXMv2Tt3B1NFjG1dXUgO/npKiFZiekkhcaYbEu6FUP9HhVnAcs7+86TbKY8FB6OJqNFrNwcioweuJ8qKR1FoVEB1m4OqJKfbj5FVWI6OXeSsa/p9VTxlCZNahYGrQ55oetP7uaIplBvlP+S0P3niS3mWsu579yycXUg5ymvNQC9RkdaVP6c+fFXIvBuXJMWL3Vnz9vfP3XcpU177xwq1INL7upYZATKo7Ly88Kqmie1171dsNEi/3eredRmrrSfhjoq/kmTXOb0GRkYdNoiPZUlTs7o0x+8ELc+Pj+fushwLJydsRk9zqRRAIMBfWz+vVoZdgtpterYDB9JRiVrFNClZ2DQ6oqMCpA6OxXpGV+YTdfncV3wJslzPkB1xnS6D11yCgatDu7r+KCJiEbm7goyGWgr5iguc/TGMjLtqSx1cX5oGSm6PI/zu2+RMncR6kJl5PjKS+Ts2k/On/mjRjVhEUgUCpzffoPM7zdUqhcx+oz8Mip8rVk4OxUZqVOYLi7/WtOGR2Dh4oLdS+P+M40C2rRMDFodMnfTc0vm6oQ2Jf2B+9r3bI/3kteImbGU3BMXSy+RZUh3rzzcTM8jmasjuoeVR4/2eC1+ndjXl5B78mLpJbKc6Yo7Z9wci4w4eRxeM0eRvuUwqb/tA0B1MwoLhTVVl04jcdWmSnU/Eh7fvXqJZaF6iaWbI3kl8LxdWZzZf4aQCyHGv+VW+e93nNydSbtvMVxHV0cyHnK9GQwG4qPy15eMvB5BVX9fXpw62NgoUJhaqSbqZiTeNao8ZS5Kl3gvIgilo1QnWLW1tcXf35/q1asbX1zfuHGD5ORkPvroI9q1a0e9evVMFhm+36lTBXObpqWlERISQr16+XOoNW3alGvXruHv71/kY2lp+chpvBePn59fkXjuNShIJBKee+45Fi5cyIULF7C0tGTr1q3GOJ555hnmzZvHiRMnaNSoERs3bnzgMaVSKUql8pHT+DQcHR3x9PTkzJkzxm06nY4LFy48YK98VlZWODg4mHyeduogAINGR/rlCDzbm86r7Nk+gJQg83NqpwaF4tnedCFFzw4BpF2KyK/8P4YmH47Fp2cL/h78Ibm3kx4v8WUsL0dFalSC8ZMUGkNWYhq12haUhVQuxe/Zetw+93Tzkd8+F4JrTW+Tba41vMmISS5mj7KnzVGRHZlg/GSExKBMSMf7vnPJQi7Fs1U9koo5lwCSz93Cq9D5590h4IH7PApLZztsq7igTEh/qnhKk0GjJftyOE4dTEf8OHUIJOvszXJKVfnLy1GRFpVg/CSHxpCdmI5fW9Nzq9qz9bjzlNeaORKJxPhwW9EZNFpyLofh2L6xyXaH9o3JCbrxRHGqbsVw9YXXuNZ1pvGTvu8sWSeucq3rTPJiK8596JFotWhDQrBsajpa0bJpc7TXzFfKzJJIkBRq+DcTCB4apgLSaskLDkHxbDOTzdatmqG+dL3Y3Wy6dcT1/Vkkz1+C8p/TRb5XX7qG3LeKcTQhgLx6VbRJyZWqQQDIL6MbIVgXLqOWzci7fK2YnfJHCLgsmE3qO0tQHS9aRhJrK9AXetGm0wESk3KrFLRaNCE3sSo0MtiqeXPyrhZfRuZI5I9ef6j0NFpU125h+9wzJpttn3sG5fngYndz6N2BKsveIGbmx2QfOVtsuEpHo0V1LRSbNqblYdOmKcoLxd+P7Ht1wGvpTOLeWk7O0X9ReZhh0GhRXr2FXVvTMrJr24Tcc8WfMw8jUVhhMBQava7X54+Iqmz3I+GxGTRasi6H42ymXpL5H6qXqHKUxEfFGT+3Q6JJTUylcbsmxjAyuYyGzzbixrnHe9aWSEBuWfxzosxSRlV/X5PGh4pIvBcR7tGX4+ffqMzfQFSrVg1LS0tWrlzJK6+8wtWrV/nggw/Mhl20aBGurq54enoyf/583Nzc6N+/PwBz5syhVatWTJ06lYkTJ2Jra0twcDD79+9n5cqVj5yeqVOn8s033zB8+HBmzZqFm5sbt27d4tdff+Wbb74hKCiIgwcP0rVrVzw8PDh9+jRJSUnUr1+fiIgIvv76a/r27UuVKlW4efMmISEhjBkzxhi/wWAg/m6vP6VSyf79+9m7dy8LFix48kJ8TNOnT2fp0qX4+/tTr149Vq5cSVpaWrELIpeFkLW7abnyVdIuRZByLpSao17AxseV8J/ye0M2ensoCi9nzs5YA0DYTwepNb4Lge+PJGLDYVyb1abG8Oc5PWWVMU6JXIpDnaoAWMhlKLyccWxYHW2OipzI/B65zywdh++ANpx46RM02Sqs7vYW12TloldpyrIIntip7/bQbmpfUiLjSY2Ip920fmhUeVz584QxzIBPXiEzPo2Dy/OH3UrlUtxr55eN1FKGvZczXg2qG39cAU5+u5sJW96j3dS+XNtxGp8mtWg2oiN/zfuu7DP5GG58u4eG0/uSGZ5AVkQ8jWb0RavMI3JrQXm0/nwyyvg0Li7ddHefvXTZ8g4Npvbmzt5zVO3WDK92DdnXv+BeJLOxwr5GwaJLdr7uODeshjo9h9yYFGQ2VgS8NZDbO8+iTEjH1tedJvMGo07NrvBTB8Wu/YvaK6eTfSmcrKCbeI3qgpWPG/E/5ffSqv72CCy9XQmdXnAvtW3oB4DU1hq5qyO2Df3Qa7QoQ/LX45DIZdjcd/1Zebtg29APXY4KVWTl6eF9v7Pf7aHN1L6kRSaQGhFPm2l90ajyuH7ftdb7k8lkxadxdHn+uWUhl+JW2wfIv9bsvFzwaFANTY7aODKgw6whhB25RFZcCpa21tTv25pqrerz25jlZZ/JJ5TwzXZqfP4aOZfCyD53E/dRXbD0cSNx/V4Aqs4dhdzbhYjXvjDuo7j/HHJxQNHQD0OeFlXoHQxqDcqb0SbH0GXmABTZXlko/9iE/Zz5aENuogm+hnXP3kg9PFDu2A6A7fiJWLi5k7U8f00j67790ScmorsdBYCsUSCKwUNRbttijFMxbCTakJvoYmOQyOVYtmyFdZduZH/xSdlnsARkbvgDtw/moA4OQX35OvYDeyHz8iDrj78AcJo2AamHGykL8kdz2nTriNuiOaSu+Ar1lWAs7vagN6jzMGTnny9Zv/+F/dD+OM+aStavW5FXq4rj+BFk/rrVfCIquKyNm3FdOJe86yGor1zHbkAvpF4eZN8tI8epE5C6u5H6/t0y6toRl4VzSf/fl6ivXi8oI1Uehpy719Sxk9iPGETezVvkXQtGVtUHh1deQnXshMkIi8oid9PvOM5/G83Nm+Rdu4ZNnz5YeHiS+2f+tWY3aSJSNzcyliwFwGZAf3QJCWij8+8tlgEB2A4bSu6W+84RmQyZn1/+v+UypG5uyPz9MSiV6GJMew9WVinfb8Xn4zdRXg1FeeEGTkO7I/d2J+2X/BEk7m+OQ+bpStzs/wF3GwSWv0nC4rUoL95A6nbv3FKjz84tt3yUlLR1W/Fe9haqq6GoLgbjOKQHcm930n/NLw+3meOQebgSPze/POx7dcD7o7dIXLIG5aViykP+f/buOrqK423g+PfG3ZWQ4O4uRVskSHF3d3cvtFiBvtBCW/hVgGKlUKCFFigUdydYQkgCcTfi+v6RcJMbA0rk0j6fc+45ZO/M3plhdmd3Z2dGC90KGSPYFdpaaNlaolu1PGlx8SR7BxR/Jt9RyPdHcPy/WcS7uBN3xxWLQc5ol7ImbM9xAOzmDUPb1hKf2RuVcfSqlwNAw0APLQtT9KqXIz0phcRnPgC8/PsGVqN7EP/Ik7i7T9Eta4/trMFEn77xXp6P3kZcXDzevlnTRvr5B+H61ANTE2Ps7QqeN/7fxG/bMapsnsrL+x5E33qK/ZB26DlYEZB5X1J20SB07S1wm5p135/9vkTH0gTDGmVJT04h7un7sU7gmzj2w+/0mdyXAC9/Arz86T2lH4kJiVw4krVG0LSNMwkPDGP35z8B0GtyHzxcnhH4IgAtbW0atG1Am94fsm3xt8o4wxeP4tbpG4T4h2BqaUrfaf0xMDLg7MG/c6VB3clzESHeXbF3ClhbW7Njxw4WLVrEV199Rf369dmwYQPdunXLFXbt2rVMnz4dd3d36tSpw++//64cBVC7dm3Onz/P4sWLadmyJenp6VSoUIH+/fu/VXpKlSrF5cuXmT9/Ph07diQxMZEyZcrg7OyMhoYGJiYmXLhwgU2bNhEdHU2ZMmX44osv6NSpE0FBQbi6urJz507CwsKwt7dnypQpjB8/Xrn/6Oho5RoDurq6lClThk8//ZT58+e/Qym+nfnz5xMYGMiwYcPQ1NRk3LhxdOzYEU3Nd3/r/5/y/f0aOuZGVJvVEz0bM6LdfLk0ZD1xvhm9r3o2Zhg4WCrDx/mEcGnIeuqsGEKFEe1JCIrg3tKf8Psj660cfVtz2p/OWjC6yqSuVJnUlZArjznfexUAFUZkLFzd5tBSlfTcnL6NF7+8HwuDXd56DG09HbqsHIG+iSG+9zzYNWQtSbEJyjCmpSxJz/YWoLGtOROOZ5XNB+O78sH4rjy/+pgdAzLKxt/Fk/3jNvHR/P60ntaTCN8QTqzYzYMjWY2qOnr89TE09XRovGYEOqYGhN714MzAz0nJVh6GDlYq5RF6y51LE7dQZ35fas/tQ8yLIC5N2EJYtiGJFnXK0/7Xxcq/G6wYAoDH/gtcm/k/0tPSMKvqSPk+LdA2MSQhOJLAy4+5NGGLym+ro9DfrqBlbozjrD7o2JgT5+rN48GrScw8/rRtzdHNsUBT3b83KP9tVKcC1r1bkuATzO1GkwDQsTNXCeMwqTsOk7oTdeURD3t9Ugy5KnzXth5DS0+HjitHoGdigP89D34e8rnKsWZSyirXsTY627HWdHwXmo7vwourT9ibeawZWpvw8cYJGNmYkfgyjmBXH/YPW8fzS2/xBnkJC//9MprmxpSa2Q9tG3Pi3bx5OnQlSX4Zo6+0bc3RKWWtEqfmX1kPCQzrVMSyV2sSfYJxaTqef6PE82dRmJhiMGQYGhaWpDz3ImrxfNKCM244NCwt0bDJdtOv0MBw1Fg07exJT0sl1d+f2O//R8Ifv2cF0dPDaNpMNK2sSU9MJNXHm5drV5J4/mxxZ69QxP11jnBTE8zGDkHTyoIkj+cET1tEakDGCFJNKwu0sj0YMe7dFYW2FpYLp2G5MGv6yJjfTxK2PGNu2dSgEIImz8di9iSM939HSnAo0fsOEb3j/ZybOv7UOSJNTTAZMxRNKwuSPZ4TOmMhqYGvysgSzWxlZNirKwotLcznT8d8/nTl9thjJwlfkdHxGP3jbkhPx3TiSDStrUiLjCT+4jWivnk/b3YTzpxFYWKC0fDhaFhakOLlRcT8+aQFZRxrmpaWaNpmdfKjUGA0bhya9naQmnGsvdz2P+J/P6oMomllhdWP3yv/Nhw4AMOBA0i6e4/w6TOKK2tF6uWfFwgyM8Zq8iC0bCxIfPoc77GfkOKfUbe0bMzRznYeNxvQCYW2FnYrJmO3Imvu68hDpwiYvzHX/t83L49fQDOzPDStLUhyf47v+GVZ5WFtgXaprGPNrH9nFNpa2H4yBdtPpii3Rx0+ReDCjI5aLRsLyh75Wvmdxeg+WIzuQ9wNF3yGFd+9YGGJOnYJLTMTbKcPQMvagoSnL3g+cgXJmW2/lo0F2g6qbX/lP7NeDjCoXQnzHm1I8g3CtcUYAII27yc9PR272UPQtrMkJSya6L9vELhhV/FlrIQ8dHVn1NSserBu8/8A6N6pHauWzC6pZBW7kN+uoGVuRJnM+5JYVx8eZrsv0cnjvqTB3+uV/zauUwGbzPuSG40m829x+Ntf0dHTYdyqiRiZGOF+7ykrBi8jITZrxgnrUtYq9yF6+nqMWzkRS3tLkhKS8Hvmy6YZX3D5aNY8+Jb2lszaMgdjcxOiw6N5eseN+T3mEOKn3jMo5EWei/w35VqPTLwTRfo/XaW1CJ07d462bdsSERGBmZlZSSfnXyctLY1q1arRr1+/fEdp5Oeg/eAiStW/x8P/0Ojzf6pSsgwHfp2yaerdsaAOLujqlXQS1F67lNiSToJaK1tdfdeUURdxYe/h1ETFTENT7S6l1Y62/ttN9fhfFBFgUNJJUHtyrL1eYsL7MRVhSap268uSToLau1rz/eu0Km4bdeRe7XXqKExKOglqb/mLPSWdhPdOV6cuJfbbx7z/KLHfLipy1fAf8OLFC/766y9at25NYmIiW7ZswcvLi0GDBpV00oQQQgghhBBCCCGEEEIUI+kU+A/Q0NBgx44dzJkzh/T0dGrWrMnp06epVq1aSSdNCCGEEEIIIYQQQgghCpQm0wcVKrXsFGjTpg1qOKvRe8vR0ZHLly+XdDKEEEIIIYQQQgghhBBClDC17BQQQgghhBBCCCGEEEIIIQB5gbyQaZR0AoQQQgghhBBCCCGEEEIIUTykU0AIIYQQQgghhBBCCCGEKEYREREMHToUU1NTTE1NGTp0KJGRkW8cf/z48SgUCjZt2vTWvy2dAkIIIYQQQgghhBBCCCHUVloJforKoEGDuHfvHidOnODEiRPcu3ePoUOHvlHcI0eOcP36dUqVKvWPflvWFBBCCCGEEEIIIYQQQgghismTJ084ceIE165do0mTJgB89913NGvWDDc3N6pUqZJvXD8/P6ZMmcLJkyfp0qXLP/p96RQQQgghhBBCCCGEEEIIobbSKbmFhhMTE0lMTFTZpquri66u7j/e59WrVzE1NVV2CAA0bdoUU1NTrly5km+nQFpaGkOHDmXu3LnUqFHjH/++TB8khBBCCCGEEEIIIYQQQuRhzZo1ynn/X33WrFnzTvsMDAzExsYm13YbGxsCAwPzjff555+jpaXFtGnT3un3ZaSAEEIIIYQQQgghhBBCCLWVVoIjBRYuXMisWbNUtuU3SmD58uWsWLGiwP3dvHkTAIVCkeu79PT0PLcD3L59my+//JI7d+7kG+ZNSaeAEEIIIYQQQgghhBBCCJGHt5kqaMqUKQwYMKDAMGXLlsXFxYWgoKBc34WEhGBra5tnvIsXLxIcHIyTk5NyW2pqKrNnz2bTpk08f/78jdII0ikg3lLyu3VC/SeYp0shvY63dkmnQP2VSyq5HvD3RbXEtJJOgtp7rjAo6SSoNROfhJJOgtq7F2VR0klQe7UMIks6CWpPU1vO168T/lLO16+jUMi10etUaRha0klQe1drzi/pJKi9Zg8/L+kkqL2H9ZaVdBLUXjtDOR+J/y4rKyusrKxeG65Zs2ZERUVx48YNGjduDMD169eJioqiefPmecYZOnQo7dq1U9nWsWNHhg4dysiRI98qndIpIIQQQgghhBBCCCGEEEJtpaf/u14QqFatGs7OzowdO5Zt27YBMG7cOLp27aqyyHDVqlVZs2YNPXv2xNLSEktLS5X9aGtrY2dnl+/CxPmRhYaFEEIIIYQQQgghhBBCiGK0Z88eatWqRYcOHejQoQO1a9dm165dKmHc3NyIiooq9N+WkQJCCCGEEEIIIYQQQggh1FZJLjRcVCwsLNi9e3eBYV43QuJt1hHITkYKCCGEEEIIIYQQQgghhBD/EdIpIIQQQgghhBBCCCGEEEL8R8j0QUIIIYQQQgghhBBCCCHUVvq/cPqgkiQjBYQQQgghhBBCCCGEEEKI/wgZKSCEEEIIIYQQQgghhBBCbaW9ZsFd8XZkpIAQQgghhBBCCCGEEEII8R8hIwWEEEIIIYQQQgghhBBCqC0ZJ1C4ZKSAEEIIIYQQQgghhBBCCPEfIZ0Caq5NmzbMmDGj0Pe7fPly6tatW+j7FUIIIYQQQgghhBBCCKG+Cm36oBEjRrBz586MnWpp4ejoSK9evVixYgWGhoaF9TPF7ty5c7Rt25aIiAjMzMyU21/ld/z48WzdulUlzqRJk/j2228ZPnw4O3bseKff+bepObsXFQZ/iI6pIWF3n3Fr0Q6in/oVGKd050bUntcXozI2xLwIxmXtL/ieuKUSpuLwdlSb2AV9GzOinvpxZ9kuQm64Kb9vsnE85fu3UokTevsZpz7+BAAdM0NqzemNXetaGJSyJDH8Jb4nbvNg3QGSX8YXUu7/uSYze1FzUFv0TA0JvOvB2aU7CH9NuVXs1Iimc/pg6mRDlHcwV9cdwONkVrlpG+rRbE4fKnRsiIGVCcEPn3Nh+W6CXDzz3N+Ha0ZRa/CHnF+xi3s/nCzU/BWFFjN6UTezzPzvevDX0h2EuudfZlaVHGg5uzd2Ncth5mjN6RW7uPmjaj7rDfmI+kM+wrS0NQCh7r5c+vIwnudcijQvRcFueEdKTeqOjo05cU998Fq2nZfXn+QZVtvGjLKfjMCodnn0ytsT8MOfPF+2XSWMzeB22PRtjUEVJwBiXDzxXrOHmHvPijwvhaHciHZUnNQVPRszXrr58WDZT4Rdd8s3vGWzqtRaPhTjKg4kBEXi/vVRnv/0t/L7MoPb4ti3JSZVHQGIdPHi8Zr9RN71UNmPnp05NZYMxPbDOmjo6RDrGcCdWd8R5eJVNBl9CxWGt6PKpC7o2ZgR/dSPe8t2EVpAmVg1q0rd5UMwqexAfFAkbt8cwzNbmQA4dGlEzXl9MSxjQ+yLYB6s/QX/41nnJYWmBtXn9KZMr+boWZsRHxzJ8/0XeLLpCGQuKuXQuSHlh36Eee1y6FoY81e7RUQ9elEkZfCuzAZ2wXx0H7SsLUh69oLg1duIv/0oz7BG7ZtjNqALutUqoNDRJunZC0K37Cbu0h1lGJ2KTlhNG4pejUpoO9gSvHobET8dKabcvL2KmXXoVdt89zV1yDqzDplm1iHXb47hkaMOlc6sQ6+uCR6s/QW/bHWowrCPqDi8HYaOGefpKDdfHm08TOCZ+wAotDSpNb8v9h/VxaiMNcnR8QRdfMj9VT+TEBRZ+IVQCMwHd8FybC+0bCxIdPcm6LP/EXcr73qkZW2O7aIx6NWsiE7ZUoTv/J2gld/lCKSJ1YR+mPX6CC07S5I8fQlat4PYC7eLITdFo7CPNdO+zph0/wjdSmUASHj0jNCNO0h48LRY8lMU7EZ0xGFSt4x2380Hr2U7iC6g3S+3fHhWu//9n3gt26ESxnZwO6z7tsYws52LcfHkxZq9xNx9P9r9vMi10evpdu6Bfq8BaJhbkOr9nNjvtpDyOO/rYK3qtTAYPh7N0k4odPVICwkk4cRREn47oBJOYWiEwdAx6DRrhcLIiNSgQOJ++Jrk29eLI0tFzn5EBxwndUfHxoxYN188lm0n+rprnmF1bMwon3ns6Ze3w+/743jmOPb+S27de8D2vQd57PqMkLBwvlyzlI9aNS/pZBWZRjN7UX1wW3RNDQm668GFJTuIeM09f/lOjWg8pw+mZWyIehHM9fUH8Mr2rGTIlY2YZF4TZfdg5ykuLtmZa3vrNaOoMeRDLi3fhYua3/ObDuyKxag+aGa2/SFrthbQ9n+A6YAu6FYtn9n2exO2ZTdxl7OufUz7OmPcrV1W2//4GWEbt7/Xbf+/UZpMIFSoCnWkgLOzMwEBAXh6erJy5Uq++eYb5syZU5g/oVYcHR35+eefiY/PemickJDAvn37cHJyKsGUqadqk7tSdVxnbi/ewV+dl5IQEkXbnxeiZaiXbxzLBhX5YOtUnh+8xPH2C3l+8BIfbJuKZb0KyjBO3ZpSf8VQHn31Gyc6LCbkuiut98zDwMFSZV/+Z+5zuM4k5ef80HXK7/RtzdG3Nefup3s5/uECrs/Yhn2b2jT+YlzhF8RbajCxK/XGdOLc0p383HUZsSGR9NyzAO0Cys2ufkU6fT0F10OX2Ou8CNdDl+j0zRRs62aVW7t1Y3BqWZOTM75ld/uFeF98SM+9CzC0Nc+1v/IdGmBXtwIxgeFFksfC1nRCVxqP6cRfy3ay4+OMMhuwZwE6BZSZtr4ukd4hnPt8PzHBkXmGeRkQzrnP97Pj46Xs+Hgpz688ps93s7Cq5FBEOSkalt2aU/bTkfh++Sv3O8wh+voTqu9ZjI6DVZ7hNXS0SQ6PxverX4l99DzPMKbNaxB6+BIP+3zCg48XkegXQvWfl6FjZ1GEOSkcDt2bUuvTYTzddISz7RcRdt2VZnvno5/jHPKKgZM1zfbMI+y6K2fbL+Lpl0eovXI4pbo0Uoaxal4dvyNXuNx7JRe6fkK8Xygf/LwAPbus40vb1JBWR5eTlpLKlcHrONN6Lg+W7yE5KrbI8/w6pbs1pe6nQ3ny5W+cyjyvttwzL/8ycbSm5e65hFx35VSHxbh+9Rv1PhuGQ7YysWhQkaZbp/Li4CVOtVvIi4OXaLZtKhbZzudVpnxMhWEfcWfRTk60movLZ/uoMqkLFUd3UIbRNNAj9MZTHqz6uegKoBAYd2qFzcLxhG/9mRc9pxB36xGl//cZWva5b8wA9BvWIu7KXfzGLeNF76nEXb9P6W+Wo1stq3w09PRI9gkk5IvtpASr9/nYMVsdOtlhMaHXXWmVR9v8iqGjNa12zyX0uisnOyzmSWYdKp2tDlk2qEizzDp0MrMONc9Rh+IDwnFZ9TN/OS/hL+clBF9+RIvtszCpnHGe1tLXwbxWWR5vPMxfHZZwefQmjMvb03Ln7KItkH/IpEtL7JaMJfSb/Xh+PI24mw9x+nFFvvVIoaNNSngUod/sJ+FJ3p2LNrOGYT7QmcBPt+LRcSIRe4/j+O1i9KqXL8qsFJmiONYMGtfm5R/n8Bm+AO8Bs0gJCKH0D6vQssm7/qo7q+7NKffpCHw3HeJe+7kZ7f7eRfm3+7raJIdF4/PlIWLz6XQ1bV6D0COXeNh7OS5dF5HoF0qNn5e+F+1+XuTa6PV0WrTFcMwU4n/ZRdT0sSQ/csFk+edoWNvkGT49IYGEPw4TvXAakZOGEbd/FwZDRqPb8eOsQFpamHz2BRo2drxcu4zICUOJ3bKetLDQYspV0bLu3pwKn47Ee9Ov3G4/j6jrT6i1dzG6+dQrReax5/3lr/kee/8l8fEJVKlYnkWzJpV0UopcvYldqTO2ExeX7ORg12XEhUTSbW/B9/y29SvS4ZspPD10if0dF/H00CU6fDMFm2z3/Ae7LmN7/cnKz+8D1wDgcexGrv2V69gA23rvxz2/UadW2CwYT9i2n/HuNZn42w9x2LaygLa/JnFX7uA3fhnefTLafoccbb9+o9q8/PMcviPm4z1wJin+wTh8v/q9bfuFeBOF2imgq6uLnZ0djo6ODBo0iMGDB3PkyBF2795Nw4YNMTY2xs7OjkGDBhEcHAxAeno6FStWZMOGDSr7evjwIRoaGnh4ZLxVqVAo2LZtG127dsXAwIBq1apx9epVnj17Rps2bTA0NKRZs2bK8K8cPXqUBg0aoKenR/ny5VmxYgUpKSnK7xUKBd9//z09e/bEwMCASpUq8fvvvwPw/Plz2rZtC4C5uTkKhYIRI0Yo49avXx8nJycOHTqk3Hbo0CEcHR2pV6+eSjrS09NZt24d5cuXR19fnzp16nDw4ME3+p20tDTmzZuHhYUFdnZ2LF++XGXf3t7edO/eHSMjI0xMTOjXrx9BQUEqYdauXYutrS3GxsaMHj2ahISE/P8ji0iVMc48+uoIvsdvEeXmy7XpW9HS16FMz/x7+6uM7UTghYc83vI7L58F8HjL7wRdekSVsc5ZYcZ1wnPfOTz3niP6mT93PtlNnH8YlYa1U9lXWlIyCSFRyk9SZNaDtyg3Xy6N/RL/U3eJeRFM0OXHuHz+Cw7t66HQLNlZtuqNdubmlt/wOHGLsKe+nJq1DW09Har0yL/c6o12xvviQ259fZQIjwBufX0Un8uPqTc6o9w0dbWp2KkRl1b/jP8NN6JeBHF94yGifUKoPfQjlX0Z2prT5rPhnJj+DWnJqUWa18LSaLQzV7b8xtMTtwh96sux2RllVr17/mUW4OLJ2dX7eHL0GimJyXmGefb3XTzO3ifcK5Bwr0AurD9AUlwCpepXLKqsFIlS4z8meN8Zgvf+Tby7H8+XbSfRPwy74R3zDJ/oG8LzpT8ScuA8qS/j8gzjPvlLAneeJO7Rc+Kf+eExZytoKDBtWasos1IoKozvzIt953ix9xwx7v48WLaLeL8wyg1vl2f4ssM+It43jAfLdhHj7s+Lved4se8cFSd2VYa5PflrvHacJurRC2Ke+XN39negocC6ZU1lmEpTPibOL4y7M7YRedeDOJ9QQi89Iu5FcJHn+XUqj++E175zeO09x0t3f+4vyzivVsinTCoM+4g4vzDuL9vNS3d/vPaew+vn81SZ0CVrn2M7EXThIa6bM87nrpt/J/jSIyplO59bNqiE/4nbBP59jzjfUPz+uEHQ+QdY1Ml6WOl98BJPNh4m6MLDoiuAQmA+oidRv/5F1MGTJHn6ELJmG8mBIZgN7JJn+JA12wj/4SAJD5+S/MKf0I07SXrhj1HbJsowCQ+fErL+B17+eZ705LzPU+qiSmYd8sysQ3eX7Sb+DerQ3cw65FlAHXqSWYeebM64JqicrQ75n7pLwJn7xHgGEuMZyIO1B0iJTcCyQcZ5OvllPOcHrMXn6HVeegQQducZdxbvxKJO+Xw7LEqS5aieRBz4i8hf/iLJw4egld+RHBCKxeDOeYZP9gsm6LP/EXX4DGkv8+5gNO3RltBvfyHm3C2SfQKJ2PsnMRfvYDG6V1FmpcgUxbEWMHcdkfv+INHVkyQvXwKXfgkaGhg0q1tMuSpcpcZ/TNC+MwRltvtey3aQ6BeG/fAOeYZP9AnBa+n2Atv9p5O/JHDHSWIfPSf+mT/PZr8/7X5e5Nro9fR69CPx1J8k/vUHqb4viPt+C6mhIeh16p5n+FRPd5Iu/E2q93PSggNJOneKpDs30a5RWxlGt11nFEbGvFy1mJQnD0kLCSLl8QNSn3vkuc/3jcP4rgTuO0Pg3jPEu/vhuWwHiX6hBR57Hku3E3zgAin51Kv/kpbNGjFt3HDat/mgpJNS5GqPdub25t/wPHGLcDdf/p65DS09HSoVcM9fZ4wzPhcfcufro0R6BHDn66P4XX5MnTFZ10UJ4S+JD4lSfsp8VI+o50H4X1MdBWVoZ07Lz4Zzatr7cc9vPrwXUYdOEn3whGrbP6BrnuFD1mwj4oeDJGa2/WGbdpDk7Y9htrY/cN46ovYdI9HVk2QvX4KWfQkaCvTf07b/3yqN9BL7/BsV6dNOfX19kpOTSUpK4rPPPuP+/fscOXIELy8v5UNvhULBqFGj2L5ddbjljz/+SMuWLalQIavn7rPPPmPYsGHcu3ePqlWrMmjQIMaPH8/ChQu5dStjiNSUKVOU4U+ePMmQIUOYNm0ajx8/Ztu2bezYsYNVq1ap/NaKFSvo168fLi4udO7cmcGDBxMeHo6joyO//vorAG5ubgQEBPDll1+qxB05cqRK2n/88UdGjRqVqyyWLFnC9u3b+fbbb3n06BEzZ85kyJAhnD9//rW/s3PnTgwNDbl+/Trr1q3j008/5dSpU0BGZ0OPHj0IDw/n/PnznDp1Cg8PD/r376+M/8svv/DJJ5+watUqbt26hb29Pd98881r/vcKl6GTNfq25gSef6DclpaUQvA1V6wbVso3nlWDigSeVx2SGnDOBauGlQHQ0NbEonY5lf0CBJ5/gFWO/do0q0ZPl2/ocnEDjdaPQdfSpMA0a5sYkBwTT3pq2hvlsSiYOFljaGOG94Ws/KUmpeB73RX7BvmXm339iipxALzPuyjjaGhpoqGlSWqOh98pCUmUalQla4NCQcdNE7iz7Y/XTlekLswcrTGyMcPromqZeV93pXQBZfa2FBoKqn3cFG19XfzuuBfafouaQlsLo9oViDx/T2V75Pn7GDesknekf0BDXweFliYpETGFts+ioNDWxKx2OYJzTAEVfP4BFo0q5xnHokElgnOcc4LPuWBWpxwKLc0842jp66KhpUVSZFZ52HWsT+R9Txp9N51OD7+lzanVlBnc9h1z9O4U2pqY53FeDcrjvPqKZcNKBOU8D59zwTxbmVg2rEhQjvN54DkXLLOVc+gNN2xa1sCovB0AptWdsGpchYC/771rtoqXthZ6NSoRe/mOyua4y3fQr1f9zfahUKBhqE9q1MsiSGDR0sinDuXVNr9i2bBS7vDnXLDIUYdyXhMEnnPBKp9jVaGhwLF7U7QMdAm7nf90Hdom+qSnpZEUpWYPYLS10KtZkdhLd1U2x1y6g379av94twodbdJztP/pCUkYNHzDuqlOiulYU+jrotDSfC+Px4x2vzyR5+6rbI88fx/jRoXX7mu+avcj1bvdz4tcG70BLS20KlYm+e5Nlc3Jd2+iVa1mPpFUaZavhHa1GiQ/vKfcptPkA1JcH2E4YSbmPx3GdMt29PsOAY33f+lDhbYWxrXLE5Hj2Is474JJIR574v1n4mSNoa0ZPhdUn5X4X3fFroD7V9v6FVXiQMY9f35xNLQ1qdzrA57sP6/6hULBR5smcG/rH6+drkgtZLb9cXm0/Xr13vD6SKFAw0CftMgC2n49XRRaWqS9h22/EG+q0NYUyOnGjRvs3buXjz76SOUhefny5fnqq69o3LgxMTExGBkZMXLkSJYtW8aNGzdo3LgxycnJ7N69m/Xr16vsc+TIkfTr1w+A+fPn06xZM5YuXUrHjhlvcEyfPp2RI0cqw69atYoFCxYwfPhw5W9/9tlnzJs3j08++UQZbsSIEQwcOBCA1atXs3nzZm7cuIGzszMWFhnDO21sbPKc63/o0KEsXLiQ58+fo1AouHz5Mj///DPnzp1ThomNjeX//u//OHPmDM2aNVOm5dKlS2zbto3WrVsX+Du1a9dWprdSpUps2bKFv//+m/bt23P69GlcXFzw8vLC0TFjTs9du3ZRo0YNbt68SaNGjdi0aROjRo1izJgxAKxcuZLTp08X62gBfZuMPCWERKlsTwiJwrB03sMnAfSszUgIjVaNExqNnrUpALoWxmhoaZIQmnu/ejamyr8Dzt7H59h1Yn1DMXSypva8vnx4YBEnnZeQlpRCTjrmRtSc0ZNnu868VT4Lm6G1GQBxOfIXFxqFST7DTgEMrM3yjGOQWW7JsQn433pK42k9CH/mR1xIFJW7N8euXgUivbJGmTSc1JW01DTu/aje8wlmZ5hZ12Jz1LXY0ChMCyizN2VdpTTDDi9HS1ebpNgEDo3fRJi7/zvvt7hoWRij0NIkOUf5JIdEopNZ3wpDmcVDSAoMJ/Kieq+38OockpijPBJDotC1Ns0zjp6NGcEhLrnCa2hroWNhTGIe009VXzKA+MBwQrK93W7oZEO54e14tu04T788gnm9CtReOZy0pBR8Dlx898z9Q/mVSUJIlPLcm5OetWmu8/urMtG1MCYhODLjfB6S43weEq2yT7ctR9E2McD54nrSU9NQaGrwcO0BfI5cLaTcFQ9Nc5OMBz9hESrbU8IiMbTKPUVbXsxH9kLDQI+Xxy8URRKLlM6rtvkt6pC+tSmBeYR/2zoEYFrVkY+OLUdTV5uU2AQuj9qY7/pFGrra1F48gBeHr5ASU/JrCGWn9aoehUaqbE8NjUTL+s3qUV5iL97BYlQP4m4+JOlFAIbN62Dcrglo5N2pqc6K61iznjWSlKAw4q7czTeMutLOt92PKtx2f0lmu39Bvdv9vMi10espTExRaGqRFqk6rUh6ZAQaZgVPh2S2/QAapmagoUn8vh0k/vWH8jtNO3s0atcj8dxpolfMR7NUaQwnzABNTeJ/zj3f+fsk69iLVNmeFBKJeSHWK/H+M8jvnj8kCuMCnpUYWJsRnyNOfLZ7/pzKdWyIrokBrgdU27v6k7qSnpqGy3tyz69p9ur6SLXtTw2LQMvqzaZnMx/ZO6PtP1FA2z971Hvb9gvxpgq1U+DYsWMYGRmRkpJCcnIy3bt3Z/Pmzdy9e5fly5dz7949wsPDSUvLePPa29ub6tWrY29vT5cuXfjxxx9p3Lgxx44dIyEhgb59+6rsv3btrKGGtra2ANSqVUtlW0JCAtHR0ZiYmHD79m1u3rypMjIgNTWVhIQE4uLiMDAwyLVfQ0NDjI2NldMbvY6VlRVdunRh586dpKen06VLF6ysVE/cjx8/JiEhgfbt26tsT0pKyjXNUF6ypw/A3t5emb4nT57g6Oio7BAAqF69OmZmZjx58oRGjRrx5MkTJkyYoLKPZs2acfbs2QJ/NzExkcTERJVtyempaCtef9NYpmdzGq0brfz7/NCMDp70nCNuFIo8NuaQ1/c5tqXnDKNQkH10j/fv15T/jnLzJfy+F91ufEmpj+rie1x10WItI31a/zSXqKd+PPy/QxSnKj2a8+GarE6030dkTKuVO3uKty+2HGX918yttFs/ljE3t5CWkkrww+e4HbmKda2yANjUKkvdkR3Z12XJP81OsajRoznOq7PK7JeRmWWWI5wiR534p8I8A/ix02J0TQyo0qkRXb8Yz+7+K9+rjgHI+5gprAFxpSZ1x6pHCx71/iTX26jqKvfxQoH1Jc9zTp47goqTu1K6R3Mu9fqMtGzlodDQIOK+J0/W7Acg6uELjKuUptzwdiXaKfDKW9eRvM45OfeTY58Kheo2x+5NKdPrA65P+pooNz/Mapah7oohxAdG8EINyuSt5VGxcpVrHoy7tMZqyhD8Jq8gNTzqteHVVn7HSb7hcwbP47h6TR0CeOnhz1/tFqFtakDpLo1p/NUEzvZamatjQKGlSbOtU1BoKLi9YMdrMlOC8rzG+edn7MDPtmG/ehoV/toK6ZDkHUDkwdOY9cl7aqf3QhEeaxaj+2DSpQ0+w+aRnvR+tGl5ylWP3qkaqXCY3B2rHh/wsNfy96bdz4tcG72BvK6XXlNK0QumotAzQKtKdQyGjyM1wI+kC5mLyCs0SIuKJPbrDZCWRqrHUzQsrNDvNeC97xR45R/d/4p/tUo9mtNmbdb96x+Z9/x5Xge99aOS/Nu/agNa4332PnFBkcpt1rXKUntUR37prN73/G/kDY8t485tsJw8BP8p+bf95qP7YNy5DT7D3/O2/1/oTa7vxJsr1E6Btm3b8u2336KtrU2pUqXQ1tYmNjaWDh060KFDB3bv3o21tTXe3t507NiRpKQkZdwxY8YwdOhQNm7cyPbt2+nfv7/yof0r2trayn+/ulHMa9urToe0tDRWrFhBr16550jV08tasCX7Pl7t59U+3sSoUaOU0xZ9/fXXub5/ta8//vgDBwfVBUl1dXVfu/+C0peenp5105xNftvfxpo1a1ixYoXKtl5GNeljXDufGFn8/rpD2N2suSA1dDKqmr6NKQnZ3qLVszLJ9SZhdgkhkbneANSzMlGOHkgMf0laSir6Od62eO1+gyOJ8w3FOHOKile0DPVos3ceKXEJXBy9kfSU4p1Pz/PUHQKzlZumbka5GVqbEpet3PQtTXK9SZBdXEgkhjnKzcDShLhsoy6iXgTza79VaOnromOsT1xwJJ2+nkK0dwgApRpXwcDKhFFXs6ay0tDSpOWSwdQb5cz2D2a+U14Li/upO/hnL7PMumZkbUpstjIzsDQhtoAye1NpyalEvMgYTRH4wAv7OuVpNNKZE4t+fOd9F4eU8Jekp6Sikzmi4hVtK9NcbzL9E6UmdKP0tN486r+CuCfqv0Daq3NI9pFFALpWpiTmU18SgiPRy1F+ulYmpCWnkJRjSoCKE7tQZVp3LvdbTfQTnxz7ieBljoeUL939KdWl8T/MTeHIKhMzle16Via5Rg+8knN0FuQuk4SQyDzDZB8NVnvpIFy3HMXnt4yO3GhXHwxLW1F1Wrf3qlMgNSKa9JTUXG8raVmakhoWWWBc406tsFs5A/8Zq4m7eq/oElmEkgqoQ/m1zfEF1KHEt6hDkHGejnmecZ6OuO+FRZ3yVB7TkVvzss7TCi1Nmv9vKkaO1pztu1rtRgkApLyqRzlGBWhamuYaPfA2UsOj8Z2wEoWONprmJqQEhWEzbyRJPkGvj6xmivpYMx/VG4vx/fEZtYjEp88LJ9HFLDmz3dfOq91/h3r0SqmJ3Sg9rReP+n36XrT7eZFro9dLj44iPTUFDXPVY01hak5aZEQ+sTKkBQUCkPrCEw0zcwwGjlB2CqRFhJGekgLZ7r1TfV+gYWEJWlqQkntE9/siOZ96pWNlSlIh3JOI99fzU3fYfy/3/atBznt+KxPiCnimERcSmWtUgL6VCfE5rosAjBwsKd2iJifGbVLZbt+4CvpWJgy7pnrP33zpYGqPdmZ3c/W4588uNfJV25/j+sjCLNfIwZyMOrXCduUMAmauJu5q3iMAzEf2xmLcAHxHLSTpqVehpVsIdVSok/UZGhpSsWJFypQpo3yQ7erqSmhoKGvXrqVly5ZUrVo1z7fwO3fujKGhId9++y3Hjx/Pc17+t1W/fn3c3NyoWLFiro/GG85TqKOjA2SMMMiPs7MzSUlJJCUlKacyyq569ero6uri7e2dKx2v3vB/k9/JS/Xq1fH29sbHJ+th0+PHj4mKiqJatYz51KpVq8a1a9dU4uX8Oy8LFy4kKipK5dPdqMYbpSslNoGY50HKT/RTP+KDIrBrlTWyQ0NbE5umVQm5lf987KG3n6nEAbBrXZvQW0+BjBv/cBcv7FqpzmVp16oWoQXsV8fcCINSFsRn6yXXMtKn7b4FpCWlcGHEFypv9BaX5NgEol4EKT/hT/2IDY7EKdvipBrampRuUpWA2/nnL+DOM5U4AE6tauUZJyU+kbjgSHRNDSjTqhaep24D4PrrZfZ0WMRe58XKT0xgOHe2/cHhoesKKcfvLik2gYgXQcpPqLsfMcGRlG2hWmZOTariW0CZ/VMKhUJ5Ifc+SE9OIcbFA7NWdVS2m7Wqzctbbu+071ITu1N6Zh8eD/qM2PvvxwJx6cmpRLp4Yd1a9Txj3bom4Tef5hkn/LY71q1Vjy+bNrWJvO+l0pFYcVJXqszsyZWBnxN5P/cFZdiNpxhVsFfZZlTejjjf0H+anUKRnpxKhIsXtjnOq7YFnFfDbrljm+tcXYuIbGUSdutZrjC2rWsTlq2cNfV1SM/RKZ+emvbOndzFLjmFhEfuGDRXHQ1o0Lw+8Xcf5xvNuEtr7NbMImDOOmLP38w3nLpLy6xDOdvmf1KHwl9Th+xa1yY0n2P1FYUCNHSyvUSS2SFgXM6Oc/3X5OrMUxvJKSQ8fIbhB6r1yOiDesTfeZJPpDeXnpRMSlAYaGli4tycmNOvvzZUO0V4rJmP6o3lxIH4jl1K4sP3Z+2gnDLafU/MWqu+1GPWujYvb75bu+8wqRuOM3vzaOBKYt6Tdj8vcm30BlJSSHn2FO16DVU2a9dtSMqTh/lEyoNCAdleeEt+/BBNeweVkWSapUqTFhb6XncIQEa9euniiXkex170Ox574v2WHJtA9PMg5SfiqR+xQZGUznHPX6pJVQILuH8NuvMMxxz3/I6tauUZp1q/1sSHRvMixzpdbr9eZn+HRfzivFj5iQkM597WPzg2RH3u+VXk2/bXI+Fu/tdHxp3bYLd6NgFzPyf2/I08w5iP6oPFxEH4jVtC4qP3t+3/N5OFhgtXkT/JcnJyQkdHh82bNzNhwgQePnzIZ599liucpqYmI0aMYOHChVSsWFE59/67WLZsGV27dsXR0ZG+ffuioaGBi4sLDx48YOXKlW+0jzJlyqBQKDh27BidO3dGX18fIyOjXGl/8uSJ8t85GRsbM2fOHGbOnElaWhotWrQgOjqaK1euYGRkxPDhw9/od/LSrl07ateuzeDBg9m0aRMpKSlMmjSJ1q1b07BhxkXb9OnTGT58OA0bNqRFixbs2bOHR48eUb58+QL3raurm2skw5tMHZQft+9PUH1qN156BvLSK5Dq07qTEp/Ei8NXlGGafjmB+MAI7mdOp/H0+xN8dGgp1SZ3xffkbUp3bIBdyxqc7vFp1n7/d5ymX00k3MWL0FvuVBjyIQYOlrj/lPEGipaBLjXn9MbnjxskBEVi6GhN7YX9SAyPUU4dpGWoR9t9C9DS1+Hq1G/QNtJH20gfgMSwaNLTSu4EcPeHEzSa3I1IryAivQJpNKUbyQlJuB3JKrcOG8cTExjBlc9/AeDejyfpc2AJDSZ2xfOv25Tv0ADHFjU40Dvr2HNqVQuFQkGEZwBmZW1psWggEZ4BPP4lY169hMgYEnIsFpeWnEpsSCSRngHFkPN/7uYPJ2g+uRsRz4MI9wqkeWaZPf4tq8y6/t94XgZGcH5dRplpaGtiVSljJI+mjhZGdhbYVHciOTZROTKg9dx+eJy7z8uAMHQM9ajWrRlOTauxf5iaXjDlw3/bUSptnkbMfQ9e3nbDdkh7dB2sCPrpLwCcFg1Gx86CZ9M2K+MY1CgLgKahHtqWJhjUKEt6cgrxT32BjGHxTvMG8nTyJhJ9QtDOHL2TGptAWlzxrV/yT3hs+5MGmycRed+T8FvulB3yIQYOVnhlnkOqL+qPnr0Fd6Z+C8Dzn/6m/KgO1Fw+hOd7zmDRsBJlBrbh1sSs8qo4uSvV5vXl9qQtxPmEKNcnSIlNIDUuY1o2j/8dp9XR5VSe1h2/369hXq8CZYd+yL05PxRzCeT2dNtxmmyeSMR9L8Juu1M+87zqmVkmNRf1R9/OnJvTtgLg8dPfVBzVnjrLB+O55yyWDSpRbmAbrk3aotyn+/cnaHN4KVUmd8X/5G1KdWyAbcsanO2edT4POHWXatN7EOcXRrSbL2a1ylJ5fCe89mUtiKZtZoiBgxX6tmYAGGd2rCQER+Y7kqEkROw4jP3nc0h46E7CvSeY9uuEtr01kT//CYDVrBFo2VgSuOALIOMhpf3aOQSv3kr8fVc0M99+Sk9IJC0mcwFcbS10KzgBGQsYatlaolu1PGlx8SR7q9d52S2zDoXf9yL0dlbb7JFZh2ot6o+BnTnXs9WhSqPaU3f5YDz2nMUqjzr09PsTfHh4KVUnd8Xv5G0cMuvQ39nqUK2F/Qg4c584vzC0jfRx6tEU6+bVuTDocwAUmhp88N10zGuV5eKwDSg0NJQjEpMiY0hLLt4Rgq8T9uNhHDbMJuGBO3F3XTEf4Ix2KWsi9mbUI5s5w9Gys8R/zv8p4+hWy7i+0zDUR8vCFN1q5UlPTibpWcYLJPp1qqBla0nCE0+0bS2xnj4IFBqE/u/X4s9gISiKY81idB8spw8jYM7nJPsFKcOkxcWTruZtWl4y2v2pxNz35OUtN+wy2/3AzHa/zKJB6Nhb4j41qx0zVGn3TTGsUZa0bO2+w+TuOM0bwNNJ71+7nxe5Nnq9hCO/YDRrMSnubqS4PkLXuSua1ja8PP47AAbDxqJhaU3MxtUA6HbuQVpIMKm+GaMjtKvXRq9nfxKOZU3Pmnj8CPpde2EwdhoJx35Fs1Rp9PsOIeHY+3k+yslv2zGqbJ7Ky/seRN96iv2Qdug5WBGQWa/KLhqErr0FblOz2rrsx56OpQmGmfUqLrNe/ZfExcXj7Zs1RauffxCuTz0wNTHG3s6mBFNW+Fx+OEGDKd2Ieh5ElFcg9ad0IyUhCfds9/wfbRxPbGAE1zLv+V1+OEmPg0uoN7ErXn/dplyHBpRuUYPDvXI8b1MoqNqvFW4HL5KeqvryTWJkDIl53PPHqfk9f8TOQ9ivnZuj7bchcn/GmiVWM0eiZWtJ4IKMqZmMO7fBbu0cgtdsJSGftt98dB8spw0j8F/S9gvxJoq8U8Da2podO3awaNEivvrqK+rXr8+GDRvo1q1brrCjR49m9erVhTJKAKBjx44cO3aMTz/9lHXr1qGtrU3VqlWVC+6+CQcHB1asWMGCBQsYOXIkw4YNY8eOHbnCmZiYFLifzz77DBsbG9asWYOnpydmZmbUr1+fRYsWvdXv5KRQKDhy5AhTp06lVatWaGho4OzszObNWRes/fv3x8PDg/nz55OQkEDv3r2ZOHEiJ08W70IyT74+hqaeDg3XjEDH1JCwux6cG7iWlNisE6yBg6XKA/jQW+5cmbiF2vP7UmtuX2JeBHF5wmaVqYm8f7+GjrkRNWb2RN/GjCg3X84PWU+cX8bbtulpaZhVdaRcnxZomxiSEBxJ0OXHXJmwWfnbFrXLYdWgIgAfX92oku7fG08ntgTf3L397TG09HRou2oEuiYGBN7z4Mjgz0nOVm7GpaxUyi3gtjvHp2yh2Zy+NJvdh6gXQRyfvIWgbMMUdU0MaD6/H0Z2FiRGxfLszxtcWX+AtGKeMqkoXNuaUWYdV45Az8QA/3se/Dzkc5KylZlJjjIztjVn9PHVyr+bju9C0/FdeHH1CXsHZKxLYmhtwscbJ2BkY0biyziCXX3YP2wdzy+9xRtSaiDs9ytomxtTelZfdGzMiXPz5smQ1ST6ZkwdpWNjjm6ORZnrnv5C+W+jOhWx7tWKBJ9g7jSeCIDdCGc0dLWp+v1clXg+G/bj88UvRZyjd+P3W8Y5pOqsXujamPHS1Zerg9cRn3nc69maYeBgqQwf5x3C1cHrqLViKOVGtichKAKXJTvx/yPrbdPyI9qjqatN4x9Uh9y6bvgV1w0ZN7qR9zy5Pmoj1Rf1p8qsnsR5h/Bg6S58D10uhlwXzPf3a+iaG1F9Vk/0bMyIdvPl4pD1ylEM+jY5ysQnhItD1lN3xRAqjMgok7tLf8IvW5mE3XLn2oQt1FzQl5rzMs7n1yZsJjzb+fzu4p3UmN+H+mtHomdpQnxQBB67zvA42/oupTo0oPGX45V/N9s2FYBHG37l8RfFuw5MQV4ev4CmmTFWkwehaW1BkvtzfMcvI8U/Y7SklrUF2qWybmjN+ndGoa2F7SdTsP1kinJ71OFTBC7MeOCrZWNB2SNZ0xRajO6Dxeg+xN1wwWfY/GLK2ZvxyaxDNTLrUNRr6lCsTwgXhqyn3oohVBzRnvjMOuSbow5dnbCFWpl1KPZFEFdy1CE9K1Oabp6Ino0ZyS/jiHzsw4VBnxOUuci3vr0FDs4NAOj49xqVNJ/ptZKQq+/+Bn5hiv7jIppmJlhNHYiWtQWJ7i/wHv0Jyf4Z52stGwu07a1V4lQ4lnUNqF+rEqbd25LkG8Sz1hnX1wpdbWxmDUXbyY602Hhizt/Cb/YXpL2MLb6MFaKiONbMBnVFQ0cbh69U51gO3bKbsC17iiFXhSv0tytomRvjOKtPRrvv6s3jwatJzDwetW3zaPf/3qD8t1GdClj3bkmCTzC3G00CwG5Ex4x2/wfVdt97wy/4bFDvdj8vcm30ekmXzhJrYor+gGFoWFiS+sKL6BXzSQvJeHlGYWGJhnXWsabQ0MBg+Fg0be1JT00lLdCfuJ3/I/HE78owaaEhRC+bg8GYyZht/pG0sFASjv5K/K97iz1/RSHktytomRtRJvPYi3X14WG2Y08nj2Ovwd/rlf82rlMBm8xj70ajycWadnXw0NWdUVOzrm/Wbf4fAN07tWPVktkllawicTfznr/VyhHomhoQdM+Doznu+Y0crFTmUw+87c5fk7fQZG5fGs/pQ/SLIE5N2kLwPdVRSY4ta2Bc2oon+8/zbxFz/ALBZiZYThqMprU5Se4v8JuwVNn2a1pboGWfdT4yfdX2L5uC7TLVtj9oUca53Gzgx2jo6FDqq6UqvxW2ZTdhX+8uhlwJUfwU6Wq0SsPly5dp06YNvr6+yoWEhXrZV2pwSSdB7QVrvWfTXJSAOCmi12qdpH7zW6ub4PTXr8nyX5f8vk27U8xqm4aXdBLU3r0oi9cH+o+rZRBZ0klQe5rab75W139VaKRhSSdB7SkUanPbqraqNCzZKQjfB49u/rveMC8KzR5+XtJJUHvf1VtW0klQe+2M5Hz0OpWfnCjpJLx3GpVqVWK/fdP/Qon9dlFRi4mwExMT8fHxYenSpfTr1086BIQQQgghhBBCCCGEEEKIIlCoCw3/U/v27aNKlSpERUWxbt37NTe3EEIIIYQQQgghhBBCiKKTnp5eYp9/I7XoFBgxYgSpqancvn0bBweHkk6OEEIIIYQQQgghhBBCCPGvpBbTBwkhhBBCCCGEEEIIIYQQeUnj3/nGfklRi5ECQgghhBBCCCGEEEIIIYQoetIpIIQQQgghhBBCCCGEEEL8R8j0QUIIIYQQQgghhBBCCCHU1r91wd+SIiMFhBBCCCGEEEIIIYQQQoj/CBkpIIQQQgghhBBCCCGEEEJtyULDhUtGCgghhBBCCCGEEEIIIYQQ/xHSKSCEEEIIIYQQQgghhBBC/EfI9EFCCCGEEEIIIYQQQggh1Fa6TB9UqGSkgBBCCCGEEEIIIYQQQgjxHyEjBcRbSUNR0klQe50MQ0s6CWrvj1irkk6C2ruirV/SSVB7E/pEl3QS1N7oX6XvvyCTAjxLOglqLyLepaSToPaqpjmWdBLUXliinK9fp6qRQ0knQe3J24Gv1++enI9e55ROVEknQe09rLespJOg9sbe/bSkk6D2Uo5sKekkiH+htHS5FihM8rRACCGEEEIIIYQQQgghhPiPkJECQgghhBBCCCGEEEIIIdSWjBosXDJSQAghhBBCCCGEEEIIIYT4j5BOASGEEEIIIYQQQgghhBDiP0KmDxJCCCGEEEIIIYQQQgihtmSh4cIlIwWEEEIIIYQQQgghhBBCiP8IGSkghBBCCCGEEEIIIYQQQm3JQsOFS0YKCCGEEEIIIYQQQgghhBD/EdIpIIQQQgghhBBCCCGEEEL8R8j0QUIIIYQQQgghhBBCCCHUliw0XLj+lSMFFAoFR44cyff7c+fOoVAoiIyMLNTf3bFjB2ZmZoW6TyGEEEIIIYQQQgghhBCisBTqSIERI0awc+dOxo8fz9atW1W+mzRpEt9++y3Dhw9nx44dhfJ7y5cv58iRI9y7d69Q9vc6Z8+eZf369Vy/fp34+HjKli1Lp06dmDVrFg4ODsWShn+jWrN7UXFwW3RMDQm768HNRTuIeupXYBzHzo2oM68PRmVsiHkRzL21B/A9cUv5vU2TKlSb1AWLWuUwsDPn/KiN+J64rbKPphvHUaF/K5VtobefcfLj5YWWt6JgOrArFqP6oGltQdKzF4Ss2Ur87Ud5hjVq/wGmA7qgW7U8Ch1tkp55E7ZlN3GXs8rCtK8zxt3aoVupDAAJj58RtnE7CQ+eFkt+CkPTmb2oNagteqaGBNz14OzSHYS9pg5V7NSI5nP6YOpkQ5R3MJfXHcDjZFYd0jbUo/mcPlTs2BADKxOCHz7n3PLdBLl4KsPM9N6d574vrNrH7W1/FE7milDzmb2oPagtuqaGBN714PRrys2ysgMfzOqNba1ymDpac2bFLu78cFIlTOnGVWg0oQu2tcphZGvOkTEbefbX7Xz2qL60mndCp20vFCbmpAV6k3jke9K8Hr82nkbZauhPXk1a4Aviv5iR7QtNtNv1QbvhhyhMLUkL8SPp2E5SXe8UXSaKSZ8ZA/hoUAeMTA1xv+vOj0u34evuk2/4xs5N6TG5D3Zl7NHU1iTQK4Bj3/3GxcPnlGF6TOpNY+emlKpQmqSERJ7edmPP2p0EePoXQ44K39wFUxg2oj+mZibcuXWf+XM+xc31WYFxTEyNWbx0Jl0+bo+pmSneL3z5ZPFaTp+6AMD0WePo8nEHKlUqT3xCAjev3+XTTzbg8cyrOLJU6JYtncWY0YMxNzflxo27TJ2+mMeP82+H/j51gNatm+fa/ueff9OtxzAAWrZowuzZE6lfrxalStnRq88ofv/9ZK4474tJc8bQZ2h3TEyNeXDnMSsXrsfDLf//7+79u7Dqq6W5ttd3akVSYhIADZrWZeTkIVSvXQUbO2umjZjHmeMXiiwPxW32gskMGd4XUzMT7t52YeGclTx9g2NvwZLpdP64PaZmJvi88GX5kvWcOfX+l8vwWUPpOqgLxmZGPLnrypeLN/P86Ys3itu2WxuWfbOYSycus3TMcuX2QZMH0LJTC5wqOpKYkMijW4/53+rv8fH0LaJcFK0Rs4bRdVBnjM2MeXLXlU2Lv3rjMvqwWxuWfbOESycus2TMJ8rtgyYPpFWOMtq2+rv3powazexF9cEZ14tBdz24sGQHEa+5zi7fqRGN5/TBtIwNUS+Cub7+AF7Z7tWGXNmIiaN1rngPdp7i4pKduba3XjOKGkM+5NLyXbj8oP7n8f4zB9JhUEcMTY1wv/uU/y3dis9T73zDN3VuRu8pfbEvY4+mthYBXv789t0Rzh86q7LPATMHqcSLCI5gVMNhRZaPwiT1qGjcuveA7XsP8tj1GSFh4Xy5Zikftcp9ffRvtP/uc3be9CQ0JpEKVkbM/bAG9Utb5Bs+KSWVbVfd+fOxP6Gxidga6TGmWUV61HIE4LeHPnxy3CVXvOszndHV0iyyfIi3IwsNF65CHyng6OjIzz//THx8vHJbQkIC+/btw8nJqbB/rths27aNdu3aYWdnx6+//srjx4/ZunUrUVFRfPHFF0X628nJyUW6/5JUfXJXqo3rxK3FOznReRnxIZF8+PMCtAz18o1j1aAiLbZOwevgJf5svwivg5douW0KlvUqKMNoGegS+cibW4tzXwxk53/mPr/Wmaz8nB26vtDyVhSMOrXCZsF4wrb9jHevycTffojDtpVo2ee+GALQb1iTuCt38Bu/DO8+U4m7fh+Hb5ajWy2rrPQb1ebln+fwHTEf74EzSfEPxuH71WjZWBZXtt5Jw4ldqT+mE2eX7mRv12XEhUTSa88CtAuoQ/b1K9Ll6yk8OXSJ3c6LeHLoEl2+mYJd3axyab9uDGVa1uTEjG/5qf1CXlx8SO+9CzC0NVeG2dZgssrn5Oz/kZ6WxrPjN4o0z4Wh8cSuNBjTib+X7mRP12XEhkTS9zXlpq2nS5R3CBfW7icmODLvMAa6BD/25u+lBR976kyrbgt0e4wh6fQvxH0xg1Svx+iP+wSFmVXBEfUM0Bs0g1T3+7m+0uk8BO1mziQe/h9xn08m+coJ9EYuRMOhfBHlonh0m9CTLmO6sX3Z/1j08VyiQiJYvGcFegXUo5jIGA5vOcDSXvOZ13EG5w78zcQNU6nTqq4yTLUmNTj503GW9JjHqiHL0dDSYPGu5ejq6xZDrgrX1BljmTh5JAvmfkqHtn0IDg7l4JHtGBoZ5htHW1ubg0e24+jkwKhh02nW0JlZ05YSEBCkDNP8g8b8+N0enNv1o2+PkWhpaXLg8A8YGOgXR7YK1dw5k5gxfRzTZiyhafMuBAaFcOLPfRgVUEZ9+o3FwbGu8lO7bltSUlI4+OsxZRhDQwNcXB4zbcaS4shGkRo1ZSjDJgxk9cIvGOA8itCQML775SsMDA0KjPcyOobWNTurfF51CADoG+jj9sid1QuL9lq2JEyePprxk4azeN5KOn3Yj+CgUPYf/h5Do/zLTFtbm/2Hv8fRyYGxw2fQslEX5kz/hMBsx977asCk/vQd25uvlm5hQpcphAeHs37v5+gbvv6cYetgw8Sl47h/LffDkjrNanNk5+9M7jaNuQMXoKmlybq9a9HTz78dUFcDM8voy6VbmNBlMuHB4Wx4qzIan2cZ1W1WmyM7f2NSt6nMGTgfTS1N1u/9/L0oo3oTu1JnbCcuLtnJwczr7G57C75etK1fkQ7fTOHpoUvs77iIp4cu0eGbKdhku84+2HUZ2+tPVn5+H7gGAI9jua+hy3VsgG29CsQEhhd+BotAz4m96TamB98t3ca8rrOICIlg+Z5P0SugHr2MfMnBzb+woOdcZnacypkDp5m6YTp1W9VTCeft9oKRDYYqPzM6TCnq7BQKqUdFJz4+gSoVy7No1qSSTkqxOunqz/ozjxnTtCI/D29BvdIWTD54g4Do+HzjzDt6lxsvwvikY22OjG7Nmo/rUdZC9VrTSEeL0xM/UvlIh4D4Nyv0ToH69evj5OTEoUOHlNsOHTqEo6Mj9eplNWqJiYlMmzYNGxsb9PT0aNGiBTdv3lR+/2qKn7///puGDRtiYGBA8+bNcXNzAzKm6lmxYgX3799HoVCgUChURiCEhobSs2dPDAwMqFSpEr///nue6Y2NjcXExISDBw+qbD969CiGhoa8fPkSX19fpk2bxrRp0/jxxx9p06YNZcuWpVWrVnz//fcsW7ZMJe7JkyepVq0aRkZGODs7ExAQoPzu5s2btG/fHisrK0xNTWndujV37qi+KapQKNi6dSvdu3fH0NCQlStXArBy5UpsbGwwNjZmzJgxLFiwgLp166rE3b59O9WqVUNPT4+qVavyzTffKL9LSkpiypQp2Nvbo6enR9myZVmzZk2e5VJcqo5x5uFXv+Fz/BZRbr5cnb4NLX0dyvbMv3e76lhnAi885NGWo0Q/C+DRlqMEXnpM1bHOyjD+Z124v+4gPsdv5bsfgNSkZBJCopSfpMjYQstbUTAf3ouoQyeJPniCJE8fQtZsIzkwBLMBXfMMH7JmGxE/HCTx4VOSX/gTtmkHSd7+GLZtogwTOG8dUfuOkejqSbKXL0HLvgQNBfrN6hZTrt5N/dHO3NjyG89O3CLsqS8nZ21DS0+Hqj3yr0P1Rjvz4uJDbn59lAiPAG5+fRSfy4+pNzqjDmnqalOpUyMurv4ZvxtuRL0I4trGQ0T5hFBn6EfK/cSFRKl8KnSoj8/VJ0R5hxR5vt9V/dHOXN/yG+4nbhH61JfjmeVWrYByC3Tx5PzqfbgdvUZqYt6dlV7nXLi84SDuJwo+9tSZduvupFw/Tcr1U6QH+5J05HvSI0PR/qBzgfF0+04i5c4F0l645fpOq0Ebkk8fIPXJbdLDg0i5cpxU17tot+lRRLkoHp1Hf8zhLQe4ceIaPk+9+Xr2l+jq6dKie6t84zy+9pCbJ6/j98yXIO9Ajm8/hrfrc6o0qq4Ms2b4p5w/eAZfdx9ePHnOt3M2Y13ahvK1KuS7X3U1fuIwNn6xlT+OnsL1iTtTJsxHX1+P3n3zPm8DDBraGzNzU4YNmsyN63fw9fHn+rXbPHqYVbf69x7Dz3sP4+b6jEcP3Zg2aSGOTg7UqVujOLJVqKZNHcOatV9x5MhxHj1yY+SoGRgY6DNwQM9840RERBIUFKL8tPuoFXFx8Rz89agyzImTZ1n2yTqOHDleHNkoUkPH9ed/m3Zw+s9zPHP1ZNHUT9HT16NLrw4FxktPTycsJFzlk92lM1fZvHYbp/88V4SpLxljJw7jyy+28efR07g9ecb0iQvRN9CjV5/8j72BQ3phZm7KyMFTuXn9Lr4+/ty4dofHD3Of1983fUb3ZPfmfVw8fonnbs9ZO3M9evq6tOvxYYHxNDQ0WLx5ITu++IkA78Bc388fsoiTB/7i+dMXeDzx5PNZG7ArbUvl2pWKKitFps/oXuzevJeLxy/h5facNTPXoaev90ZltGTzIrZ/sZMA74Bc388bspAT2cpo7az1700Z1R7tzO3Nv+F54hbhbr78PTPjerFSAdeLdcY443PxIXe+PkqkRwB3vj6K3+XH1BmTda+WEP6S+JAo5afMR/WIeh6E/7UnKvsytDOn5WfDOTXtG9KSU4ssn4Wp6+huHNzyC9dOXMX7qTdfzdqIrp4urXq0zjfOo2sPuX7yGr7PfAl8EcixH4/y/MlzqmW7NgJITUklMiRS+YkOjy7q7BQKqUdFp2WzRkwbN5z2bT4o6aQUq123vOhZy5FetZ0ob2nMvA9rYGesx4F7eY/suuwVzC2fMLb0bkTTslY4mBpQy96Mug45RhYowMpIT+Uj1EtaenqJff6NimRNgZEjR7J9+3bl3z/++COjRo1SCTNv3jx+/fVXdu7cyZ07d6hYsSIdO3YkPFz1ZmXx4sV88cUX3Lp1Cy0tLeV++vfvz+zZs6lRowYBAQEEBATQv39/ZbwVK1bQr18/XFxc6Ny5M4MHD861bwBDQ0MGDBigkl7IeLjep08fjI2NOXDgAElJScybNy/P/GZfRyAuLo4NGzawa9cuLly4gLe3N3PmzFF+//LlS4YPH87Fixe5du0alSpVonPnzrx8+VJln5988gndu3fnwYMHjBo1ij179rBq1So+//xzbt++jZOTE99++61KnO+++47FixezatUqnjx5wurVq1m6dCk7d2a8sfvVV1/x+++/88svv+Dm5sbu3bspW7ZsnnkqDkZO1ujbmhFw/oFyW1pSCkHXXLFumP9FslWDiipxAALOuRQYJz+2zarR2+VrPr64nibrR6NrafLW+yg22lro1ahE3GXVTqS4y3fQq1ftzfahUKBhoE9a5Mv8g+jpotDSIi0q/zDqwtTJGkMbM15cyKoPqUkp+F13pVSD/OuDff2KKnEAnp93UcbR0NJEQ0uTlBwPvlMSkijVqEqe+zSwMqHch3V5+PO5f5ib4mPqZI2RjRnPc5Sb73VXHAoot/8ETS00Slck5eldlc0pbnfRLFs132hajT5Cw9KepL/25fm9Qkub9BTV+pSenIRmuTc8dtWQjaMt5jYWuFy8p9yWkpTC4+sPqdwg/7LKqeYHtbEv78CT63lPgwZgYJzxZm9MZMw/Tm9JKFO2NLZ2Npw7c0m5LSkpmSuXb9K4cb184zl3+pBbN+7x+RfLeOR+mQtXjzJj9ng0NPK/bDMxNQYgIiKq8DJQDMqVc8Le3pZTp88rtyUlJXHh4jWaNWv4xvsZOXIA+3/5jbi4/N8Qe1+VLlMKa1srrpy7rtyWnJTMrat3qduoVoFxDQz1+evWYU7f/Z2vd2+gas3KRZ1cteBUpjS2dtacP3tFuS0pKZmrl2/RsEndfON16NSW2zfus2bDElyeXuDsld+YNmtcgcfe+8DeyQ5LW0tunc/qsE9OSub+NRdqNKxeQEwYNnMIkeGR/PnziTf6LUOTjLcuowu41lRH9k72WNpacvN81pSHyUnJ3LvmQo2GBXe2vm0ZGWWW0Us1LyMTJ2sMbc3wuaB6r+Z/3RW7Aq4XbetXVIkD4H3eJd84GtqaVO71AU/2n1f9QqHgo00TuLf1j9dOM6MubJ1ssbCx4N6FrOvIlKQUHl1/SNW3uDaq9UFtHCo48PiG6rWRfblS/HBzB1svfc+sLXOxdbIttLQXFalHorAlp6bxJDCKZmVVZ0toWtaa+34RecY59yyIGram7LjhSftvT9Pt+3P839nHJOToJIpPSqXTtjN0+PZvpv56E9eg9+u6Woi3VahrCrwydOhQFi5cyPPnz1EoFFy+fJmff/6Zc+fOARlv53/77bfs2LGDTp06ARkPtE+dOsUPP/zA3LlzlftatWoVrVtn9KovWLCALl26kJCQgL6+PkZGRmhpaWFnZ5crDSNGjGDgwIEArF69ms2bN3Pjxg2cnZ1zhR0zZgzNmzfH39+fUqVKERoayrFjxzh16hQA7u7umJiYYG9v/9q8Jycns3XrVipUyHibccqUKXz66afK7z/8UPVNk23btmFubs758+fp2jXrzaVBgwapdKT079+f0aNHM3LkSACWLVvGX3/9RUxM1gOSzz77jC+++IJevXoBUK5cOR4/fsy2bdsYPnw43t7eVKpUiRYtWqBQKChTpsxr81OU9GzMAEgIUT3RJoREYVg6/2k69KzNSAjNESc0Cj1r07f6/YCz9/E+doNY31CMnKypPa8P7Q4s5LjzUtKSUt5qX8VB08wEhZYmKaGqDV1qWARaVvnPnZed+cjeaBjo8fJE/vPiWs8eRUpQGHFX7uYbRl0YWJsBEJejPsSFRmHskH8dMrQ2yzOOQWYdSo5NwP/WU5pM60H4Mz/iQqKo0r059vUqEOGV9xQC1fu0JDk2gWfvwRvyhpnlFpujDGJDozApoNz+CxSGJig0NUl/GamyPf1lFApjs7zjWNmj03U48VsWQFpanmFS3O6i3bo7qR4PSQ8LRLNSHbRqNoH3+EGTWeY5PCokUmV7VGgU1g55T2n2ir6xAVuv/4CWjjZpqWn8sHQbDy7lnnbplWFLR/HkxuMC5+NVRzY2GeUQHBymsj0kJBRHx1L5xitT1pEWrZry64GjDOw7jvIVyvD5hmVoamrxxbqv84zz6aqFXLtyC9cn7oWXgWJgZ2sDQFBQqMr2oKAQyjiVfqN9NGpYl1o1qzFu3JzXB34PWVlnTOeX8y3/sJBwSpXOfQ38itez5yyZthL3J88wNDZkyNj+7Dr6P3p/OBRvr/zX/fg3sLHNaMtCglXrVWhwKKULPPZK80GrJhw6cIwhfSdQrkIZVm9YiqaWJhvXfZtvPHVnYZ1xnRgRGqmyPSI0AluH/B8q1mxYg84DnBnTYcIb/9akZRNwuf6A527P/0lSS4yFdcb0kBE5rrPfpIy6DOjEmA7j3/i3XpWRl5qXUb7X2SFRGBdwr2ZgbUZ8jjjx2a6zcyrXsSG6Jga4HlC9P6k/qSvpqWm4/Pj+zP1ullmPInMca5GhkVg72BQY18DYgO9v7EA789rof0u+5X62Fy/c7z7ly5kb8ff0w8zajL5T+7Pm0Hqmt5us1h1MUo9EYYuITyI1PR0LQx2V7ZaGuoTGJuYZxy8ynrt+EehoafJ/PRoSGZ/E6lMPiUpIZkWnOgCUszDi0061qWhtQmxSCntvezFi7xX2j2hFGfP8p7QU4n1WJJ0CVlZWdOnShZ07d5Kenk6XLl2wsso64Xt4eJCcnMwHH2QNcdLW1qZx48Y8eaI61Kt27drKf796KB8cHPza9QmyxzM0NMTY2Jjg4OA8wzZu3JgaNWrw008/sWDBAnbt2oWTkxOtWmVMf5Ceno5CoXijvBsYGCg7BF6lOfvvBgcHs2zZMs6cOUNQUBCpqanExcXh7a36oKNhQ9W349zc3Jg0SXWeuMaNG3PmzBkAQkJC8PHxYfTo0YwdO1YZJiUlBVPTjIZzxIgRtG/fnipVquDs7EzXrl3p0CH/YeeJiYkkJqqeVJPTU9FW/LM51cr2bE7jdVkdHeeGbgAg5ygchULB69YOyT1yR5HXxgK9+D3rjbsoN1/C7nvR48YmHD6q+9pph9SK4s3ybty5DZaTh+A/ZQWp4Xn3eJuP7oNx5zb4DJ9HepL6rWVRtUdzPlqTVYeOjMioQ7nqyxvUoZzfK3KU44mZW+mwfizjbm4hLSWV4IfPcT1yFZtaZfPcXY1+rXly+Eq+0+qUpGo9mtM+W7kdyqfc3uTY+8/IdWIi77JRaKA3ZA5JJ/aSHpL/IriJh79Dr98UDBZ8A+mQHhZA8o3TaDduV6jJLkoterRi7OqJyr/XjsyY2i7Pw+8156SEmHjmdZqJnqE+tT6ozbAlowj2DuLxtYe5wo76bBxOVcvySZ+F75yHota778d8sWmF8u9B/TIfEuUoD4VCUeBpW0NDQWhIGLOmLSUtLQ2Xe4+ws7NhyrTReXYKfL5hGdVrVKar86A89qZeBg7sybdff678u1v3jAUSc9aZjDJ6sxPSyJEDefDwCTdv3Su0dJakLr078sn6+cq/Jw2eDbx9GbncfoTL7ay3TO/ecOHA6Z0MHtOXNYv/r5BTXbJ69e3Kuo3LlX8P7Z/xEPtty0yhoUFYSDhzp3+Scezdf4ydvQ0Tp456rzoF2vX8kFlrZyj/Xjg8Y12NXHlXKPJdsE/fUJ9FX81nw7yNREe82RQl01dOpUK1ckztNfMfpbs4tev5IbPXZqVzwfDFQN51Jr+LI31DfRZ/tYD18/6PqLcqo/JM7TXjH6W7KFXq0Zw2a7OuF/94h+vFvO7V8jv2qg1ojffZ+8QFRSq3WdcqS+1RHfmls3qvCdOqR2smrJms/HvViMyXAf9BmxYfE88s5+noGepR+4M6jFw6mkDvQB5lXhvdOZc1isXb7QVut1359uJ3tO3zIb9//1sh5ejdST0SxUWB6jO6jOd2eYdNy/xudde6GOtqAzCnbTXm/HaHhe1qoqetSe1S5tQulbV+YF0HcwbsvMTPd54z/6P3b3rOfytZaLhwFUmnAMCoUaOYMiVj4Zuvv1a9gX11Is/5oD2vh+/a2trKf7/6Li2fNzHzi/cqbkHxxowZw5YtW1iwYAHbt29n5MiRyt+rXLkyUVFRBAQEvHa0QF6/m73hGjFiBCEhIWzatIkyZcqgq6tLs2bNSEpKUolnaJi7JzKv8nrlVd6+++47mjRpohJOUzPjIX79+vXx8vLi+PHjnD59mn79+tGuXbtc6ym8smbNGlasWKGyradRLXob184z/Ov4/nWH0LseWenSyah++jamJGRbtFTXyiTX6IHsEkIi0c/xhoCelQkJoe82p2JCcCSxvqEYl8//rbuSlBoZTXpKKlpW5irbNS3MSAnLe5jcK0adWmG7cgYBM1cTdzXvEQDmI3tjMW4AvqMWkvTUq9DSXZg8Tt0hIFsd0tLNqEMG1qbEZqtDBpYmud5GyS42JDLXWyb6libEZatDUS+COdBvFVr6uuga6xMbHEnnr6fkuV6AQ+MqWFQsxR+Tt/zTrBWpZznKTTOz3Azfstz+C9Jjo0lPTUVhonqcKYxMSY+JzB1BVx9Np0poOJRHt1fmA2CFAoWGBobrD5Ow7RNSn7lAbDQJ21eDljYKQ2PSo8LR6TqctPD3Z/HKW6du4H73qfJvbZ2M9s7M2ozI4KxzkImlKVE53pDLKT09naAXGXNTv3jshUPF0vSY1DtXp8DIFWNp0K4xy/stIjwwLK9dqZUTx89w53bWiAcdnYw3mGxsrQgKyjp3WFlZ5nqDObugwBCSU1JUrlvc3TyxtbNBW1ub5OSszsc165bQsdOHdOs8hAB/9a9PR4/+xY0bWe2Qrm5GGdnZWRMYmPUShY2NFUEFlNEr+vp69O/XjeUrNhR+YkvI2RMXVR7m62TevFrZWBKabdSJhZV5rtEDBUlPT+fhvSc4lXMsvMSqiZPHz3DnVtYCrzq6r449a4KzjUKxtLYkJCT/c0lwUAjJyXkde9a5jj11dvmvqzy+66r8WyfzfG1hbU54cFadMbc0IyIk72vIUmVKYe9kz+rtnym3KTQy7kVOPz/BsNYj8X+RNX/+1M8m07xDU6b3nk1owOuP3ZJ2+a+rPMlWRtrKMrJQKSMzSzPC8ykjh8wyWrN9pXLbqzL6+/lJhrYeoVJG0z6bwgcdmjGt9yxC1LCMnp+6w/57ue/VDKxNict2vahvZUJcAfdqcXldZ1uZEJ/HvZqRgyWlW9TkxLhNKtvtG1dB38qEYde+VG7T0NKk+dLB1B7tzO7m6tHxdOPUDZ5mvzbSfXVtZE5Etmsj0ze8NgrMrC/PH3tRuqIjvSf3VXYK5JQYn8gLt+fYl8t/9FNJkHokipq5vg6aCgVhOUYFhMclYWmgm2ccKyNdbIz0lB0CAOUsjUgHgmIS8hwJoKFQUMPeFO8I9V53Uoh3UWSdAs7OzsoH3R07dlT5rmLFiujo6HDp0iUGDcp4qy05OZlbt24xY8aMN/4NHR0dUlMLZ6GYIUOGMG/ePL766isePXrE8OHDld/16dOHBQsWsG7dOjZu3JgrbmRkpMq6AgW5ePEi33zzDZ07ZyxY6ePjQ2jo6y8Kq1Spwo0bNxg6dKhy261bWW+z29ra4uDggKenJ4MHD853PyYmJvTv35/+/fvTp08fnJ2dCQ8Px8Ii9/QzCxcuZNasWSrbDlV586GxOaXEJhATm6CyLT4oEvtWNYl4mLEgjIa2JrZNq3J31f589xN6+xl2rWri+l3WvJ32rWsRcuvdpkzQMTfCsJQF8dneLlArySkkPHLHoHk9Yk5nzZFr0LwesWeu5RvNuHMbbFfNJGDOWmLP38gzjPmoPlhMGIjf2MUkPlLfqSeSYxOIylGHYoMjKdOyJiGPsuqQQ5OqXFqbfx0KuPOMMi1rcveHrDpUplUt/G/nzntKfCIp8YnomhpQplUtLq35OVeYGv1bE+TiSegT9ZzaJDk2gcgc5RaTWW7B2cqtdJOqXCig3P4TUlNI832GVuW6pD7IOq60Ktcl5VEex09iHHHrpqhs0v6gM5oVa5Owc23uh/4pyaRHhYOGJlq1m5Ny7xLvi4TYBBJiVReZjAgOp3aLujx/lNGRqKmtRfUmNdm7dufb7VyhQEtHtVN95KdjadyxKSv6LyHEJ++RfuomNiYWrxjVG4egwGBat/2ABy4ZIyG1tbVp/kEjPl2e/0PsG9fv0KtPV5UXCypULEtgQLDKQ8m165fSuWt7enQZivcL3yLIUeGLiYklJkcZBQQE0e6jVty7l/EgXFtbm1Ytm7Jw0erX7q9vn27o6uqwZ++hIklvSYiLjSMuNk5lW0hQKM1aN8b1YcbDJy1tLRo2q8fGz/KeTio/VWtUwt3V4/UB3zOxMXHExqi2wUGBIbRq04yH2Y69Zh80ZNUn+Y+SuHntLj37dlE59spXLJPr2FN38bHxxMeqrq8RFhRGw1YNePYo4/9fS1uLOk1r87/V3+e5D28Pb0Z+NFZl2+i5IzAwMmDzJ98Q7J/V0Tlt5RRaOH/AzL5zCPTJvRixOoqPjccvzzKqz7NHz4CMMqrbtDbbVn+X5z4yymiMyrbRc0eib6TPlhxlNH3lFFo4t2BG39lqW0bJsQkk57zODoqkdMuahGa7XizVpCpX1+R/vRh05xmOLWvi8n3WdbZjq1oE5nGdXa1fa+JDo3nx9z2V7W6/Xsb3kup8+l13z+Ppr5dx/SX/aVCLW0JsPIE56lF4cDh1WtbF65EnkFGPajSpyU9veW2kUGR1VuVFS0eL0hUdeXLj8dsnvAhJPRJFTVtTg2p2plx9EcKHlbNe6Lz+IpQ2FfOe7q2ugwWn3QKIS0rBILOj6kV4LBoKsM1nMeH09HTcgqOpZGVc+JkQ/1h6+utfEhdvrsg6BTQ1NZVTAb16U/0VQ0NDJk6cyNy5c7GwsMDJyYl169YRFxfH6NGj3/g3ypYti5eXF/fu3aN06dIYGxujq5t3z+DrmJub06tXL+bOnUuHDh0oXTprHltHR0c2btzIlClTiI6OZtiwYZQtWxZfX19++uknjIyM+OKLL97odypWrMiuXbto2LAh0dHRzJ07F319/dfGmzp1KmPHjqVhw4Y0b96c/fv34+LiQvny5ZVhli9fzrRp0zAxMaFTp04kJiZy69YtIiIimDVrFhs3bsTe3p66deuioaHBgQMHsLOzy7dDQ1dXN1d5/tOpg/Lj+v0JakztRrRnEC+9Aqk5rRsp8Uk8P5z10LvZl+OJD4zg3ppfMuOcpP2hJVSf3BXfk7cp3bEBdi1r8FePrDeZtAx0MS6X1SAYOVpjXsOJxMhY4vzC0DLQpdacXvj8cZP4oEgMHa2pu7AvieExaj11UMTOQ9ivnUvCQ3cS7j3BtF8ntO1tiNz/BwBWM0eiZWtJ4IKMh03Gndtgt3YOwWu2knDfFc3MUQbpCYmkxWQ8cDAf3QfLacMInPM5yX5ByjBpcfGkxyXkkQr1cueHEzSa3I0IryAivQJpPKUbKQlJuB7JqkMdN44nJjCCy59n1KG7P56k34ElNJzYFY+/blOhQwOcWtTgl95ZdahMq1qgUBDhGYBZWVtaLhpIhGcAj3JcQOoY6VO5S2MurNxbPBkuJHd+OEGTbOXWJLPcnmQrt06Z5XYxs9w0tDWxrOQAZLz1Y2xrgXV1J5JjE4l8kfHwW9tAF7OyWceeqaM11tWdSIiM5aW/+r/pDZB8/jd0B80k1ecZqc9d0W7WEYW5NclXjgOg02UYChMLEvdtgvR00gJVH0Slx0SRnpKksl3DqTIKU0vS/DxRmFqi03EgKBQknXm/H2T++cNRekzuQ8BzfwK9AugxpQ+JCYlc+i3rOJn8f9MJDwxj37rdAPSY1BsPl2cEvQhES0eLem0b0KpXG35YslUZZ/TK8XzQrRXrx64mPjYe01fz0UbHkZyoOrJO3W379idmzBqPp8dzPD1eMGP2eOLjE/j1wDFlmC1bPycwIIiVKzIeVm7/YR9jxg1l9eeL+W7bbipUKMP02eP5ftsuZZzPv/iE3n26MmwkaqeRAAC660lEQVTQJGJiYrGxyZimMTr6JQkJec+nqq6+2vw9C+ZPxf2ZF8+eebFg/lTi4uLZ9/NhZZjtP36Jv38Ai5esVYk7auQAfvv9JOHhud/kNTQ0oGLFcsq/y5V1ok6dGoSHR+Djk/90X+po1//2M3b6cLw9fXjh5cPY6cNJiE/gj0N/KcOs3ryM4MAQNq3KmOZm4uzR3L/9EG8vHwyNDBkyth9ValZm5cKsDil9A32cymVd8zo4laJKjUpERUYT6Kf+I08K8t23PzFt9ji8PF/g6fGCabPGER+XwKGDWcfeV1vXEOgfzOpPM1762fnjz4waN5jPPl/Ej9t2U65CGabNGscP2/aUVDYKzcEfDjN4ykB8vfzw9fJjyNSBJMQncvrIGWWYhZvmERIYyvdrfyQ5MTnXugAx0Rkdetm3z1g1lY96fMiS0Z8QFxOHeeac6rEvY0lKeL/O1wd/OMSQKYPw9fLDz8uPwVMHkRCfkKOM5hMaGMp3a38gKTE517oAMdEZ6715qZTRNNr1+JDFo5cRHxOnXL8g5j0oI5cfTtBgSjeingcR5RVI/czrRfds14sfbRxPbGAE1zKvF11+OEmPg0uoN7ErXn/dplyHBpRuUYPDvT5T3blCQdV+rXA7eJH0VNWHPImRMSRGxqhsS0tOJS4kkkjPANTZsR9+p8/kvgR4+RPg5U/vKf1ITEjkwpGsBXCnbZxJeGAYuz//CYBek/vg4fKMwBcBaGlr06BtA9r0/pBti7OmLRu+eBS3Tt8gxD8EU0tT+k7rj4GRAWcP/l3seXxbUo+KTlxcPN6+Wdc0fv5BuD71wNTEGHu7gtexeJ8NbViOxX/co4adGbVLmfHrfR8CouPpUydjmvGvLrgS/DKBlV3qAtC5Wim+u+rOsuP3mfhBZSLjk9h43pXutRzR0854xrX18lNqlzLHydyQmMRk9t15ztPgaBa2q1lS2RSiyBVZpwBkvJWen7Vr15KWlsbQoUN5+fIlDRs25OTJk5ibm+cbJ6fevXtz6NAh2rZtS2RkJNu3b2fEiBH/OL2jR49m7969Kgv8vjJp0iQqV67Mhg0b6NmzJ/Hx8ZQtW5auXbvmepu+ID/++CPjxo2jXr16ODk5sXr1aubMef3CeIMHD8bT05M5c+aQkJBAv379GDFiBDduZL25OmbMGAwMDFi/fj3z5s3D0NCQWrVqKUdfGBkZ8fnnn+Pu7o6mpiaNGjXizz//RKMEF7p8/PUxNPV0aLxmBDqmBoTe9eDMwM9JyfZ2gaGDFelpWVMlhd5y59LELdSZ35fac/sQ8yKISxO2EJZtehSLOuVp/+ti5d8NVgwBwGP/Ba7N/B/paWmYVXWkfJ8WaJsYkhAcSeDlx1yasEXlt9VNzPELBJuZYDlpMJrW5iS5v8BvwlJS/DPeotW0tkDLPqvxN+3fGYW2FrbLpmC7LOtt5qjDpwhalNGRZTbwYzR0dCj11VKV3wrbspuwr3cXQ67eza1vj6Glp8NHq0aga2JA4D0PDg3+XOUNFeNSqnUo4LY7f07ZQvM5fWk+uw+RL4L4c/IWArMNddU1MeCD+f0wsrMgMSoW9z9vcHn9AdJSVEcnVenWFBQKXH+7WvSZLUQ3Msut3aoR6JkYEHDPg4M5ys0kR7kZ2Zoz/ETWm7uNJnSh0YQu+Fx9wv7+qwCwq12e/r9kHXttP8k49h4euMCJ2f8r6mwVipR7l8DAGJ0O/VGYWJAW8IL47z4lPSLjjT+FsTka5gUvpJuLtja6nQajsLSDxARSntwifu9GSHi/h6P+vvUwOnq6jF45HkMTI57de8rqIctJyFaPLEtZk5atHukaZIS3tLckKSEJPw8/tszYyNVjl5VhOgztBMDyX1ap/N43s7/i/MEzvE82b/oOPT1d1n3xCaZmpty5dZ++PUcRm+1t+dKl7UnPNl2Jv18gfXuO4rM1Czl/5XcCAoL4butPfLUx623VUWMyRlr+9qfqeXrqxAX8vPcw75P1G75BX1+PLV+txtzclBs37tKpyyCVEQVOjqVyTQNZqVJ5WrRognOnAXnut2GDOvx9OmuKxC82LAdg50+/MHrM+zVlwI9bdqGnp8uSz+diYmqMy51HjOs/XWVEgb2DncqxZmxqxPINC7CyseTlyxhcHzxlRI8JPLyb9VZpzbrV2H74G+Xf8z+dAcCRn/9gyfQcD17eM19/+QN6+nqs2bAMUzMT7t52YUCvMcTGZJWZQ2l7lXrl7xfIgF5jWLF6AX9fPkJgQBDfb93Nlk15v03/Pvn5m/3o6ukwY9VUjE2NeXLPlbmDF6iMKLBxsFGpQ2+i+/BuAGw6qPqC1NqZ6zl54K+8oqitfd/sR1dPl5mrpmFsaszje09ylZGtg43K+fpN9Mgsoy8Pqo5SWTtzHSfUvIzuZl4vtlo5Al1TA4LueXA0x/WikYOVyrS2gbfd+WvyFprM7UvjOX2IfhHEqUlbCL6nOkrJsWUNjEtb8WT/ef5NDn/7Kzp6OoxbNREjEyPc7z1lxeBlJGSrR9alrFWusfX09Ri3cmLWtdEzXzbN+ILLR7NGlFraWzJryxyMzU2IDo/m6R035veYQ4hf7qlN1Y3Uo6Lz0NWdUVOz1iFatznjfqt7p3asWjK7pJJV5DpWLUVkfBLbrrgTGptIRSsjtvRuRClTAwBCYhIJeJl1zBnoaLG1bxPW/v2IwbsuYaqvQ4cq9kxuUUUZ5mViCp/99YDQ2ESMdLWoamPCDwOaUcverLizJ0SxUaS/6Spu/wF79uxh+vTp+Pv7K+cBVmft27fHzs6OXbt2vT5wIdlTakix/db7qpGp+s0Rqm7+iLV6faD/uMKZGO3fbUKfd1tH5L9g9K8l1+n7PjgT+aSkk6D2IuJjXh/oP66q+b9vjv7CFpYo5+vXqWrkUNJJUHuyuODr9VOo59po6uSU4r+9ftabaJ9u+vpA/3Fj735a0klQeylH1HO9PXWiPyb/KQ1F3spY/rM1TgvDizCX1wd6zxTpSIH3RVxcHF5eXqxZs4bx48erZYdAXFwcW7dupWPHjmhqarJv3z5Onz7NqVOnSjppQgghhBBCCCGEEEIIId4T8gohsG7dOurWrYutrS0LFy4s6eTkSaFQ8Oeff9KyZUsaNGjA0aNH+fXXX2nXrl1JJ00IIYQQQgghhBBCCCGKTHp6eol9/o1kpAAZC/QuX768pJNRIH19fU6fPl3SyRBCCCGEEEIIIYQQQgjxHpNOASGEEEIIIYQQQgghhBBqK03WFypUMn2QEEIIIYQQQgghhBBCCPEfIZ0CQgghhBBCCCGEEEIIIUQxioiIYOjQoZiammJqasrQoUOJjIx8bbwnT57QrVs3TE1NMTY2pmnTpnh7e7/Vb0ungBBCCCGEEEIIIYQQQgi19W9caHjQoEHcu3ePEydOcOLECe7du8fQoUMLjOPh4UGLFi2oWrUq586d4/79+yxduhQ9Pb23+m1ZU0AIIYQQQgghhBBCCCGEKCZPnjzhxIkTXLt2jSZNmgDw3Xff0axZM9zc3KhSpUqe8RYvXkznzp1Zt26dclv58uXf+vdlpIAQQgghhBBCCCGEEEIItZWWnl5in8TERKKjo1U+iYmJ75Sfq1evYmpqquwQAGjatCmmpqZcuXIl7zJIS+OPP/6gcuXKdOzYERsbG5o0acKRI0fe+velU0AIIYQQQgghhBBCCCGEyMOaNWuU8/6/+qxZs+ad9hkYGIiNjU2u7TY2NgQGBuYZJzg4mJiYGNauXYuzszN//fUXPXv2pFevXpw/f/6tfl+mDxJCCCGEEEIIIYQQQggh8rBw4UJmzZqlsk1XVzfPsMuXL2fFihUF7u/mzZsAKBSKXN+lp6fnuR0yRgoAdO/enZkzZwJQt25drly5wtatW2ndunXBGclGOgWEEEIIIYQQQgghhBBCqK10im7B39fR1dXNtxMgpylTpjBgwIACw5QtWxYXFxeCgoJyfRcSEoKtrW2e8aysrNDS0qJ69eoq26tVq8alS5feKH2vSKeAEEIIIYQQQgghhBBCCPGOrKyssLKyem24Zs2aERUVxY0bN2jcuDEA169fJyoqiubNm+cZR0dHh0aNGuHm5qay/enTp5QpU+at0imdAuKtaKWXXK/c+yIwwrikk6D2bujGlHQS1N7wBL2SToLaSw2JLekkqL1KirzfLhAZapg1LOkkqL3dOm6vD/QfN0GnYkknQe1F6cn14+uYpuU9RFxkkcXwXm9I55CSToLaCz7x+oc0/3XtDENLOglqL+XIlpJOgtrT6jGlpJMg/oXS/2XPJKtVq4azszNjx45l27ZtAIwbN46uXbtSpUoVZbiqVauyZs0aevbsCcDcuXPp378/rVq1om3btpw4cYKjR49y7ty5t/p9ubYSQgghhBBCCCGEEEIIIYrRnj17qFWrFh06dKBDhw7Url2bXbt2qYRxc3MjKipK+XfPnj3ZunUr69ato1atWnz//ff8+uuvtGjR4q1+W0YKCCGEEEIIIYQQQgghhFBbaSW4pkBRsbCwYPfu3QWGyWuExKhRoxg1atQ7/baMFBBCCCGEEEIIIYQQQggh/iOkU0AIIYQQQgghhBBCCCGE+I+Q6YOEEEIIIYQQQgghhBBCqK1/20LDJU1GCgghhBBCCCGEEEIIIYQQ/xEyUkAIIYQQQgghhBBCCCGE2kqTkQKFSkYKCCGEEEIIIYQQQgghhBD/EdIpIIQQQgghhBBCCCGEEEL8R8j0QUIIIYQQQgghhBBCCCHUliw0XLhkpEA+ypYty6ZNm94qzrlz51AoFERGRhZJmoQQQgghhBBCCCGEEEKId6H2IwUUCkWB3w8fPpwdO3YUGP/w4cP06NHjndNy9+5dVq9ezYULF4iKisLJyYnWrVszd+5cKleu/M77/7fqH7Anz+33Pt2L27d/5PmdQkuTalO7Ua5fS/TtzHnpEcD9VT8TeNblndNjWtWR+quHY1G3AkmRMXjsOsPjjYeV31s3q8aHh5bkivdnyzm8fBbwzr9f2OxHdMBxUnd0bMyIdfPFY9l2oq+75hlWx8aM8suHY1S7PPrl7fD7/jiey3YUb4KLSe8ZA/hoUAcMTQ15dted7Uu34evuk2/4Rs5N6TG5D7Zl7NHU1iTQK4A/vvuNS4fPKcN0n9SbRs5NKVWhNEkJiTy97ca+tTsJ8PQvhhz9c44j2lNu8sfo2pgR4+aL69KfiMinjgCYN6tG1RVDMapSmsSgCLy2HMXnp9MqYbRMDKi0sD+2XRqjbWpIvHcIrst3Efr3PQDKT+uObefGGFYqRWpCEpE3n/L0s73EeqjfMZQXnY+6odulHwpTS9L8nhO/+xtSnz54bTzNSjUwXLyRNF8vYpaMV27XcCiDXu8RaJatjIa1HfG7vybp5KGizEKx+XBGbxoO/BB9U0N87z3j6NLtBLv75RveppIDH83qS6la5TAvbc0fn/7E1R9PqITR0NTgwxm9qdPjA4yszXgZHMndg+c5t/nIe/l2SOsZvag/6EP0TA3xu/uM40t3EFJAGVlXcqDN7D7Y1yyHmaM1J1fs4nqOMmo9oxetZ/ZW2RYTHMn/NZpcJHkoalPnjqPfsJ6Ymhpz/84jVsz/nGdunvmG7zmgK59vXp5re83SzUlKTMq1ffz0EcxeMoUd2/ayesn/FWbSi0Tjmb2oMbgtuqaGBN314PySHYQ/zb/OAFTo1Igmc/pgWsaGqBfBXFt/AM8Tt5TfD7uyERNH61zxXHae4sKSnQDoW5nQfNEAnFrVQsfEAP/rblxYupOo50GFm8Ei0HJGL+plHmf+d59xYukOQgs4zqwqOdB6dh/sMo+zv1bs4maO4yy75pO60XZ+f278cJxTn+4uiiwUupKqR1N88i6fyyv3cXdb3tf+JaXRzF5Uz1ZGF5bsIOI1ZVS+UyMaZyuj6+sP4JWtjIbkU0YPdp7iYmYZNZrZi4rdmmJUyoLUpFRCHnhxfd0Bgu95FG4GC5l2667oduiLwtSCNP8XJPyyldRnD18bT7NCdQxmbyDN/zmxKydl7a9Ze/RHzMkVPnpyV0hJLtS0F6c2M3rRIFu7/8cbtPttZ/ehVOb56MSKXVzL43xkbGtO+4UDqNimDtp6OoR5BvLbvP8R8PB5Eeam8JkO7IrFqD5oWluQ9OwFIWu2En/7UZ5hjdp/gOmALuhWLY9CR5ukZ96EbdlN3OXbWfvr64xxt3boVioDQMLjZ4Rt3E7Cg6fFkp+isP/uc3be9CQ0JpEKVkbM/bAG9Utb5Bs+KSWVbVfd+fOxP6Gxidga6TGmWUV61HIE4LeHPnxyPPezlOszndHV0iyyfKiDW/cesH3vQR67PiMkLJwv1yzlo1bNSzpZ4h9K4/27F1Rnat8pEBCQ9QBp//79LFu2DDc3N+U2fX39YknHsWPH6N27Nx07dmTPnj1UqFCB4OBgDhw4wNKlS9m/f3+R/XZSUhI6OjpFtv+i9lvtSSp/239Yh0b/NxbfP27kG6fW/L6U6f0Bt+Z8T/Qzf+za1OaDH2byd7flRD588Y/TomWkT+v9Cwi+/JjTnZZiVMGOJpsmkBqXiNu2P1XC/vHBbFJexiv/TgyL/se/W1SsuzenwqcjebbgO6JuumE/tD219i7mVquZJPqF5gqv0NUmOSwa7y9/pfS4riWQ4uLx8YSedB7Tja1zviLA05+eU/uyaM8KZrWdREJsQp5xYiJjOLzlAP4efqQkpVD/o4ZM2DCV6LBIXC7cA6Bakxr89dNxPO+7o6GlSf+5g1m4azlz200lMT6xGHP45uy6N6PaZ8N5vOAHIm644TisHQ32LeBSy9kk+IXlCq/vZE2DvfPx3X0Gl8lbMG9cheprR5MUFk1Q5jGr0Nak0S+LSQyN4t7ojSQEhKNXypLUmKzjxbxZNby3/0XUPQ8UmhpUWjSAhvsXcanVHFLj1LOsXtFu0ga9IZOI3/EVqe4P0WnbFcO5a3i5YBTpYcH5R9Q3RH/8AlIe3UHD1FzlK4WOHmnBASTfuIDe4IlFnIPi03LCxzQf3YlDc7YR6hVAm6k9GbF7EZs+nE1SPseatr4u4d7BPPzzOp2XDsl3v40Gt+PX2d8S7O6LQ63y9Fo/noSX8Vzdnv9DO3XUfEJXmo7pzG9zthLmGUjLqT0YsmchX7edU2AZRXgH8/iP63RYlncZAQS7+bBr8Brl3+mpaYWe/uIwdupwRk4cxIKpK/Dy8GbSrNFsP/g1zk17Exsbl2+8l9ExdGym2jGSV4dArbrV6Te0J64P34+HA/UndqXu2E6cnrWNSK9AGk7rTve9C9jdei7J+dQZu/oV6fjNFK5vOIjHiVtUcG5Ix2+mcKjXZwRlPmT8pesyNDSzBglbVClNj30L8TiWdT3W5fuZpKak8sfojSS9jKfu2E5037eQvR/OJ0VN2zmAZhO60mRMZ47O2Uq4ZyAfTO3BoD0L2foGx9mTP67TvoDjDMC+dnnqDWpL0ON/fg1a3EqyHv1YX7VzskzbOny4fgwex/O/9i8J9SZ2pc7YTpzJLKMG07rTbe8C9hZQRrb1K9Lhmync2HAQzxO3KO/ckA7fTOFwr8+UD/QPdl2GIlsZWVYpTbccZRTpFcDFpTuJ9g5GU0+HOmM68fGe+expOZuE8JdFm/F/SKtha/T6TSBh7xZSPR6h3aoLBlNXErN8LOkRIflH1DNAf+RcUl3vojAxz/V1enwsMctGq258jzsEPpjQlWZjOnMks91vNbUHw/YsZPMbtvsd8zkf6ZkYMPrXT/C6+pg9w9cRGxaNeRlbEqLzbyfVkVGnVtgsGE/QZ1+TcOcRpv0747BtJc8/HkdKQO56pN+wJnFX7hC6cQdpL2Mw6dkBh2+W4z1gBolPMo45/Ua1efnnOULuPiYtMQmL0X1x+H41Lz4eT0pw7vsddXfS1Z/1Zx6zqH1N6jqYc/C+N5MP3uDQqNbYm+T9/Gve0buExSbyScfaOJobEB6XRGqa6nWhkY4WR0a3Vtn2b+8QAIiPT6BKxfL06NyBmYtXlnRyhFAraj99kJ2dnfJjamqKQqFQ2bZ3714qVKiAjo4OVapUYdeuXcq4ZcuWBaBnz54oFArl3x4eHnTv3h1bW1uMjIxo1KgRp0+fzuPXM8TFxTFy5Eg6d+7M77//Trt27ShXrhxNmjRhw4YNbNu2TSX87du3adiwIQYGBjRv3lylE+NNfrts2bKsXLmSESNGYGpqytixYwH47rvvcHR0xMDAgJ49e/J///d/mJmZqcQ9evQoDRo0QE9Pj/Lly7NixQpSUlKU3y9fvhwnJyd0dXUpVaoU06ZNe+P/i38qISRK5VPKuQHBlx8T653/xWPZPi148tXvBJy5T6x3CB4//U3geReqTOisEq7qpK50ubaR3p7b6Xh6NaW7NC4wLWV6NUdTV5sbM7YR5eaL35+3ePzVb1Qe3ylX2MTQaJV0p6epX4+kw/iuBO47Q+DeM8S7++G5bAeJfqHYD++QZ/hEnxA8lm4n+MAFUl6+XxeQb6PT6I85suUAN09cw/epN9/O/hIdPV0+6N4q3zhPrj3k1snr+D/zJdg7kBPbj+Ht+pwqjaorw6wd/ikXDp7B190H7yfP2TpnM9albShXq0JxZOsfKTuhC757z+K75yyx7v64Lv2JBL8wnEa0zzO847D2JPiG4br0J2Ld/fHdcxbffWcpNymrE6n0wLZomxtxd8QXRN58SoJvKJE33Hj52FsZ5vbAtfjtP0+Mmy8vH3vzYPq36DtaY1K7XJHn+V3pdOpD0vnjJJ//kzR/bxL2fENaWDA6H31cYDz9UTNJvvo3qc8e5/ou1cuNhJ//R/K1s5D8/t7o5tR8lDPnv/6NxydvEvzUl19nf4u2vg51uuf/9o2fiycn1+zlwdGrpCSl5BnGsX4lXE/d4unZe0T6hvLo+A2eXXyAQy31rz85NRntzMUtR3A9cYuQp778Nnsr2no61CygjPxdPDm9eh+Pjl4jNTHvMgJIS0kjNiRK+YlT0wdJrzN8/EC+3bidv/44i7urB/OmfIK+vh5dezsXGC89PZ3Q4DCVT04Ghvps2PoZS2etIirq/SifOqOdubX5NzxP3CLczZfTM7ehpadD5R7515k6Y5zxufiQ218fJdIjgNtfH8X38mPqjMkqw4Twl8SFRCk/ZT+qR+TzIPyuPQHArJwddg0qcX7RdoLvexLpGcD5xdvRMdSlcvdmRZ7vd9F4tDOXtxzBLfM4O5p5nNUo4DgLcPHkzOp9PD56jZQCjjNtA126fzmJP+Z/T0JUbFEkv0iUVD0CVL6PC4miXIf6+F55QnQB1/4lofZoZ25nK6O/M8uo0huU0Z3MMrrz9VH88iij+JAo5afMR/WIeh6Ef7Yycj9yFd9Lj4j2DiHiqR+XP92DrokBltWcijTP70K3XS+SL58k+fIJ0gJ9SPxlK2kRIei0LvhFI/0h00m+cZZUzyd5B0hPJz06QuXzPms62pkLW47w5MQtgp/6cjjzfFTrNe3+qdX7eFhAu99i4sdEBYTx29z/4Xffk0jfULwuPyLCu4AXVtSQ+fBeRB06SfTBEyR5+hCyZhvJgSGYDci7HoWs2UbEDwdJfPiU5Bf+hG3aQZK3P4ZtmyjDBM5bR9S+YyS6epLs5UvQsi9BQ4F+s7rFlKvCteuWFz1rOdKrthPlLY2Z92EN7Iz1OHAv747py17B3PIJY0vvRjQta4WDqQG17M2o65BjZIECrIz0VD7/BS2bNWLauOG0b/NBSSdFFIL09PQS+/wbqX2nQEEOHz7M9OnTmT17Ng8fPmT8+PGMHDmSs2fPAnDz5k0Atm/fTkBAgPLvmJgYOnfuzOnTp7l79y4dO3bk448/xtvbO8/fOXnyJKGhocybNy/P73M+mF+8eDFffPEFt27dQktLi1GjRim/e9PfXr9+PTVr1uT27dssXbqUy5cvM2HCBKZPn869e/do3749q1atypXOIUOGMG3aNB4/fsy2bdvYsWOHMtzBgwfZuHEj27Ztw93dnSNHjlCrVq03LO3CoWtlQqmP6uK573yB4TR0tEjN8bZfanwS1o2rKP+utaAv5Qa04vaCHznRZh5u/ztB0y0TsW5WNd/9WjWsRMhVV9KyPYgKPOeCgb0FhjmG+XY4tYpu97bQ5peF2DSvnnNXJU6hrYVx7fJEnLuvsj3ivAsmjarkE+vfz8bRFnMbCx5cvKfclpKUwpPrD6ncIP+6kVOND2pjX94B1+t5D2UFMDA2ADJGGagjhbYmJrXLEXpOdaho6HkXzBrmPeWZWcNKhJ7PEf6sCyZ1yqPIfJPEpmMDIm89pfraUbR9uJUPzq+n/PQeoJH/dG/amWWVrKZlpaSphWbZyqQ8uKWyOeXhbbQq1cg3mnbLjmjY2JN4+KeiTqHaMHe0wdjGnGcXs+pLalIKz68/wanBu02p533LjfIf1MSynB0AdtWcKNOwCk/P3Xun/RY3M0drjG3M8byYNfVUalIKL6674tig0jvv36KcLTNvbGHqpY302jwFszymq1B3jmUcsLG14tK5a8ptyUnJ3Lhyh/qNaxcY18BQn7N3jnLh/h9s27ORarVyt32ffD6fc6cuc+WCer2hnB8TJ2sMbc3wvpBVZ9KSUvC77op9AXXGrn5FlTgA3uddsMsnjoa2JlV6fcCT/VnXY5q6GQOIUxKzOi7T09JJTUrFvrH6TpNp5miNUR7Hmfd1V0oXwnHm/NkInp25x/PL+V8PqJuSrEc56VuZUObDujzZf+7tMlHEXpWRT44y8r/umm9+IWOkgM9bllHl15SRhrYmNQa3JTEqljB1HY2iqYWGUyVSHt9W2Zzy+DaaFfK/T9Ju3gENa3sSjxUw5ZauPkarf8Jo7W70J3+KhqP6vmzzOuaZ7b5HjvPR80Jo96u0b4C/ixd9v5nG3NvfMP7PVdQf0PZdk1y8tLXQq1GJuMt3VDbHXb6DXr1qb7YPhQINA33SIvPv6Ffo6aLQ0iLtPXkZILvk1DSeBEbRrKzqNV3Tstbc98u7w+zcsyBq2Jqy44Yn7b89Tbfvz/F/Zx+TkJyqEi4+KZVO287Q4du/mfrrTVyDooosH0KI94PaTx9UkA0bNjBixAgmTcqYnmbWrFlcu3aNDRs20LZtW6ytM06kZmZm2NnZKePVqVOHOnXqKP9euXIlhw8f5vfff2fKlCm5fsfd3R2AqlXf7IHiqlWraN06Y1jWggUL6NKlCwkJCejp6b3xb3/44YfMmZM1v+KSJUvo1KmTclvlypW5cuUKx44dU/ndBQsWMHz4cADKly/PZ599xrx58/jkk0/w9vbGzs6Odu3aoa2tjZOTE40bF/xmfWEr168VyTEJ+P55s8BwgeceUGV8Z0KuuRLzPBjbljVwcG6AQiOjH0tTX5fK4zpzru8qwm4/AyDWOwTrxpWpMOQjQq7mPV+6no0ZsT6qbyklhERlfmdKrE8ICcH/z96dx9WU/38Af932fZMSk1ZLkSH7MsiarbKbEioMg4gMxghRGlu2GbJWzNhGjCXbhMhOWmhRUhGVkHbV7X7/qK6u2zbfmelzct/Px+P7+Omcw7w6v3vPPfd8Pp/3Oxv33ffifdRzSMnJwHBcH/Q/vgxXx3rhzZ2a67A3NFktVfBkpFHyJltke/GbbGg21WCSiQvUdTQAAB8+Oy8fsj5Au0XtD8wUVZXw6919kJGTRRm/DAdW+CE6LLLG4x1XOCPuXgxePq1+QJE1OS01SMlIo/iN6A1f8ZsPkK84T5+T19Go9ngpWRnIaaniY2Y2FA10oNWnHV4H3cRD+5+hZNwM5uucwZOWwrPN1dfJb+vpiHd34pAX9/Jf+d3+KzxVdfCkpcVmqQk+vAdPvfo6nlK6LaAwcQby1y4Ayhpn+Zb/h0pTdQBA3mevl7w3OdD4Svsf/dvXd56BvKoS5odshIBfBp60FP7aeAxRp2//o3+3oalUvM/EzlHWB2i0+GfnKC3iGU4tLC+Voqythm/m2cE5aBV2Dl6CQq4PvlWhrdMEAPD2s1n+b9+8RXN9vRr/XlJCMpbOW42nsYlQUVXGlJnf4sjZfbCx+hYpSeX9Y0bYDYG5RVuMHTLlv/sF/mVKFZ/fhVmir5nCNx+gWsv7SqmphvjfyfoA5Yr36eeMh3aBvJoS4o5fF257n/gaOS/eoOeSibi2bB9KCj6i44zhUNbVgHINnxlcUJkt/7P3WX7WB6j9w/eZ+ageaNbeCPttVvyjf6ehsXwdfa7tuG9Qkl+EZ+cf1HgMC5XnqOCz37fg/zxHSjWcI6NazpHBwI4Y8stcyCjKIT8zG2ccfkbRe25ev3kqahX3R9ki2wW52dWWBAIAKZ3mkB/tjIINi2q8PypLf4GigI3gpyWDp6AEuQF2UP5hM/LXzEZZJrd7dlVHpZbrkfo/vB5p6jdF18kDcXvvedz45U+0+NoEw1ZPAb+4BJFBYf/o324o0hpq4MlIozRL9D6b//Y9ZLRrrpdflabTWEgpKSD3Qs3XnaaLnFGa8RYFtx79o7wsvC8sBl8ggJayaPnoJsryyMqvvoxfWnYhHqW9h5yMNDbbdUF2YTG8Lz/Gh6ISrB5W/uzJSEsFnsM6wLSpGvKLS/H7w+eY9vstHJ3WFwaayv/570UI4aZGPSgQGxuLmTNnimzr3bs3tm7dWuvfy8/Px+rVq3H27Fm8evUKpaWlKCwsrHGlwN9dJtKhw6eZbXp65V9oMzMz0bJly3r/t7t06SLyc3x8PEaPHi2yrVu3biKDAg8fPsT9+/dFVhDw+XwUFRWhoKAA48ePx5YtW2BsbAxra2sMHz4co0aNgoxM9S+Djx8/4uNH0Q+eEgEfsrya684ZjOmFzus/1YS87rAeWXc/lU8y+rYfUoNuouxj7eUzHnkEosvG6Rh2YyMgECAvOQPPj1yH0aTy8i/qrVtARlEO/Y4uE/l7UrIyyK5otGR97WcoVdzUZ92Nx3WH9eUHffb/T2Ez64rNuc9eI7dKM9S3DxOh2LwJ2swazqlBgUpiL08er5qNX67edn0x3ftTjfb1TuV1Aqs7LXWdl6K8Qiwd5gYFZUW0790Bk39yRkZqBmLviDdQc1ozEy3bGmLVuGXV/Etc89nvzavjuvb5vsq3SMV2npQUirNy8HjRbqBMgJyo51DQ1YThnFHVDgqYrXOCqpkB7tis/Ce/RMMSewGh+tcPTwqK3/+Ij0H+KEvn9oDHP/W1bW/YeH+6vh90Lr+mir1c6np91YPFqJ7oaNcHx+f/gsynL6FnboDhHo7IzXiPRydu/KN/+7/U3q4XRlY5R4edNlR7HI/H+8fnKLHqKrF44GV4IuZd34yvx32DO3vP/6N/+780aqw1PDf9KPx5pv0CAIAA4p/NtZ2jyIePEfnw07X54d1InLpyCI7TJ2LtjxvRrLkulnstgvOEudX2GeCK1na90N/n04rSs9M2Aqj+s72ul4z4/prPofmkfki5Gon8jGzhtrJSPs5/txUDNszAjMe7UVbKx4uwJ0i+ElGv36WhtLPrheFV3mdHa3if/dP7IVU9LQxeOQWHHX3Ar+O+lTUuvY7EjpnYD09P3mJ+Dlt9do7OVZwjsVskHk/8HuAzf+ccmU3qh9SrkSio5hyl3YrFUevlUNRUgbm9FYb8OhcnbFahkIN9zD6p7gapGjwpKLosxcczB1GWWXODXf7zOPCff/p+VfjsCZSX/wJZK1t8PLrzX8j737Kw64VRVa5Hv1Vcj8RfIv/8+xlPSgqvopMQsuEYACD9SQp0Wn+FLo6DGs2gQI3qeX5Uh/dHkzmT8WruavDfVT/LXdNlHFSH98eLqT9AUMzta3dteJ+9twQCAXg1vN3KKvZ5j+wIVXlZAIC7lRnc/wzHskHtoSArjQ7NNdGh+acBvI4tNDEpIAxHwpOxZGDNq6EJ4ZoyCXrW1RAa9aAAUOWBboXyi2XNJSwAYPHixbh48SI2btwIU1NTKCoqYty4cSgurv5LY+vW5Uum4+Li0LNn3TVVZWVlxfKVVcyOqO9/W1lZdLS2ut/r85vPsrIyrF69GmPGjBHLpKCgAH19fcTHx+Py5cv466+/8P3332PDhg0IDQ0VyVxp3bp1WL16tci2scrtMV615uX8aRfD8Tb8mfDnwvR3wj9rd28DNdPmuP3d9hr/fqWPb3Nx08kXUvKykNdUQWH6e3RYPulTH4KKMiU3HDeg8PVnMw0qPvyvT94AqYpyJ/yi8vNblJkNhc9m0ctrq5Xve1P9jQUAvA1PhOFYbtWgK3mXC0EpH3Kfzd6T01ZHcVbNv8uX5uHle0h89Kl5pKxc+WtZo6kGsjM/vTbUmqjjQ1Z2rf+WQCBARko6ACAl5jmam34F2+/Hig0KTFs9A50HdcPqCT/iXTp3m1cVv8tBWSkfcp+95uW01cVWA1T6mJld7WuqrKQUJRWz1z5mvEdZKR+o0mcjL+EVFHQ1wZOVhqDKUlUz72nQGdoF9+xW4ePrd+A6Qe4HCPh88D5vFKymWX2NW0VFyBi3hbRBKyhMqejRwuOBJyUFNf9LyF//A/gxEf998AYQ+9dDvIhIFP4sI1d+C6Gqo468KitzlLXVkP8Pr0HWy+xxfedpRJ8pXxmQEf8CGi200fd7W04PCjy9HA6/R58+AyvPkUpTdeRlZgu3Kzf55+focyWFH5EZ/wJahs3qPpihKxeuIzL80zVVTq58Jpy2jjbeZHy6nmppa+Htm/pfMwQCAaIfxcDQWB8A0P7rttDWaYKgvz71mpKRkUHXnp0w2WUC2rfoJbw3Y+n55XBhA1cAkK54zSg1VUdBldeMorYaCmu5Tyl4ky02U1lRWw0FWeIPF1VbNMFXfdrj/MwtYvveRCfjqPVyyKkqQkpWBkXvcjHu9CpkRj3/e7/Yfyjhcjj2PhI/Z8r/8vtMz8IIKk3V4XL2U1NCKRlptOzeFl2mDoFPq6mc6TfFtddRJb1ubaBp2hwXvt9Rz9/kv5N8ORxH63mOCv6Pc1RYzTlSqThHF2o4R6WFH5GTnIGc5AxkPHoG++sbYTapH8J/OfM3frOGIcjLKb8/+mxVAE9Vvfr7IwVFSBu2gYK+KRQmVTSerrg/Uv01GAVbl4EfX81qXIEA/OSnkNZp8R/8Fv+++MvhSKvmelTd537eP/zcz83MxpsE0QGWN4lpMBvW9R/9uw2Jn50DQSkfMtqiryNpLQ2Uvq29l4TKsL7QXbsAr928UXC7+hUAmk5joTVzEl46L0PxU+58bv0dmopykObx8PazVQHvCorRREm+2r+jrSIPHRUF4YAAABg1UYEAQEZeUbUrAaR4PLTTU0fq+8bTK4cQ8u9r1IMCZmZmCAsLw5Qpn5aF37p1C2Zmn+rRycrKgs8XraV248YNTJs2TTjzPi8vD8nJyTX+d4YMGQJtbW2sX78eJ0+eFNufnZ0t1legJn/3v12pbdu2uHdPtB7ugweiy3AtLS0RHx8PU1PTGv8dRUVF2NjYwMbGBnPmzEHbtm0RHR0NS0tLsWOXLVuGhQsXimw73Xqm2HFVleYXIS+/qNp9xt/2x7vIJGTH1L/UStnHEhSmvwdPRhpfjeiKF2fuAgBynqaBX1QMpRbaNZYKKniZJbYt60ECOiybCClZaZRVPLhs1s8CBa/fiZUVqkqzvSEKa5kFxYKgpBS5UUnQ7NcBb89/em1o9OuAtxdqL8/0JSnKL0JRfrrItveZ72DRpyOSn5TfDErLysCse3sc9gn4W/82j8cTDjJUmuY5A12H9sCaiT/hzQtuN/YSlPCRE/Uc2v0skHn+02tCu68FMi9Wv4w/+0ECdIaIXg+0+3dATmQSBKXl75n395+i+ejeIrN6lE30UJT+7rMBASfoDu+Ke6M9Ucix5oI14peCn/wUMu07o/ThTeFmmfadURJ+U/z4wgLkLnMR2SQ30AYy5p1QsH01yt6ki/+dRqo4vwjvPru+52a+h0kfC7x+Ul4DWVpWGobdzXDJ5/A/+m/JKspVO/Bd16A/a8X5RSiu5hwZ97FAesU5kpKVhkH3tvjL58i/+t+WlpOBtmkLpN6Lr/tghvLzC5D/XLTRfWZGFnr3647Y6PLssrIy6NbLEhs8655EUJVZ+9aIjy1/OHP7+n2M+GaiyH6fbR5ISkjB7u0BnBgQAICS/CJ8+Ow1k5+RDf1v2iOrymumRfe2uLXuaI3/Tnp4IvS/aY/IvReE21r2tUD6wwSxY80m9ENhVg6SQyJq/PeKcwsBAOqGutDpYIy7G//4O7/Wf6q691le5nsY9bFARpVz1rJ7W1z5B++z5JtPsHvwEpFtIzfOxNtnr3F75xnODAgA3H0dmU/qh8yoJLyNZV9msSS/CCXVnKOvPjtHzbu3xe1azlFGxTmKqnKO9Os4Rym1nKOqeDwepOXEJ2pxAr8UZakJkDGzRGnELeFmGTNLlEZWU9qvqAB5q0W/N8r1GwXpth1R6LcGZVk13x9J6xuDn5b8byX/T9V2b5Qucm/UFpf/4ef+i4dP0cRYtKxeEyM9fEgT/87LWSWlKHqSAKVenZD316fXkVKvTsi/cqfGv6Y6vD90vdzw2t0H+aHV9wjSdB4HrVnfIm3Gcnx8Iv5+bCxkpaVg1kwdt1PeYEDrTxM97qZkob+pbrV/p2MLLfwV/xoFxaVQqhiYSnmXDykeoFtDM2GBQID4zBy00lb9938JQv5Dn68uJv9Mox4UWLx4MSZMmABLS0sMHDgQZ86cQVBQEP766y/hMYaGhggJCUHv3r0hLy8PTU1NmJqaIigoCKNGjQKPx8OKFStq/XKorKyMvXv3Yvz48bCxsYGrqytMTU2RlZWFY8eOITU1FUeO1O9D/u/+tyvNmzcPffv2xebNmzFq1ChcuXIF58+fF3lA4uHhgZEjR0JfXx/jx4+HlJQUoqKiEB0djbVr18Lf3x98Ph/du3eHkpISDh48CEVFRRgYGFT735SXl4e8vOhodG2lg2ojo6II/VHdELH692r3d982CwXp7xHtXX4TrtXJBIp6msh+nAJFPS20XzQGPCkpxP1SXi6pNL8IcbuC0Wn1ZPB4PLy5Fw9ZVUVod2mN0vwiJB+vfiZp6slbaLdoDLptmYXYbX9CxbgZzFxtEbP502BP6xnWyH/xBh/iX0JKVgYG4/pAf2Q3hLn4/l+/+38pze8s2myfh9zIZ8h58BR6kwdBoYU2XgdeAgAY/mgPeT0txM/7NENLuZ0hAEBaWQFyTdSg3M4QgpJSFDz9ckqfnN93BrZzxuF18iukP38Nu7njUFz0ETf//FR7cvbm+Xif/hZH1pc3PrP9fiySohKRkZIOGTkZdLTqjG/G9Mf+n3YJ/47z2u/Qy6YvNs3wRmF+IdQr69HmFKCEo+UpknedQ4cdc/AhMgnZD55C33EQFL7SRmpA+XWy9fJJkG+mheh5vwIAXgReRkuXIWi72hEvDoVAo0trfGVvhchZ24T/5gv/yzBwGQozr6lI2XsRysbNYDzfFilVviCb+zhDb0xvhE/diNK8QshVzKorzS1AWRG3l/IWn/8DirOWgv/8KfiJMZCzGgGpJjooDimfuSc/wQVSmtoo9PsZEAhQ9jJZ5O8LcrKBkmLR7dIykGpRca2VkYGUpjakWpoARYWNsmZupVv7L6DfHFu8TU7H2+fp6DfHFiWFxYj889MXvbGbZiMn4x0ury+/vkvLSqNpq68q/iwDNV0tNDM3KP9inZIBAIgLCUe/ObbITstCZsJL6LUzRG+X4Xh4/FqD/47/1N19F9Bnjg3eJqfj3fN09Jlri5KiYjyuco5sN89Cbvp7XKk4R1JVz5GcDFSbaUK34hy9rzhHg5fb4+lf4fjw6i2Um5T3FJBXUUQkh1dS1CTA7zBmLXBCSlIqkpNeYNYCJxQWFuHsiU/XlPU7ViMjPROb1v4CAJjrPgMRD6ORkvQCKqrKcJwxCW3bt8HqJeVlrfLzC5AQ90zkv1NQUIT377PFtnNN5L4L6DLXBh+SM5D9PB1d5tqgtKgYT099es0M8v0O+envcfvnYxV/5yLG/PETLGePRNKlhzAe0hlf9WmHoDFrRP9xHg9tJ/RF3B83IOCL33+ajOiGore5yH2VhSZt9fHNKkc8v/gAL66Ll9Hjknv7LqD3HBu8r3if9ap4nz2p8j4bVfE+u1bP91lxfhHefHZvVFLwEYXvc8W2cxHL1xEAyKoownREN4Stqf7enwui9l1A54pz9OF5OiwrzlFClXM0sOIc3ak4R1H7LsLuj5/QafZIPL/0EEYV5+hkDecovppzJKMoj86utki+9BD5mdlQ0FRF+ymDoNxME4nn7v7nv/f/6+NfQVB0Wgx+ylPwk2Ih+81wSGnpoPj6OQCAvJ0TeBraKPLfUH5/9Eq0abIgN7v8/qjKdrmRDuAnxaEsM03YU0BK3wSFh39pyF/tX3Vn3wV8U+Vz/5uK61F0levR6M2zkJP+HiHV3RtVXI8+vze6vfc8XIJW4ps5Nnhy9i5adDRBZ3srnFm2r+F/yX/gfUAQ9HwWo+hxAooiYqE+YRhk9XSQfbT8daTt5gQZ3SZIX1pe4kt1eH8083FH5rpdKIqMg3TFKgNB0UeU5ZVPMtB0GYcmrlOQ7v4zStIyhMeUFRRCUFD9hEUuc+xihOXnItCumQY6NNfAicgXeJ1TiHFftwQAbLseh8zcIqwd0REAMNysOfbcToDH+UjM7t0a2YXF8A2Ng62FPhRky5/f7Lr5FB2aa6KlpjLyPpbgcHgynmbmYNmg9qx+zQZTUFCI1Jefvm+lvcpA3NNnUFdThV4zHYbJCGGvUQ8K2NnZYevWrdiwYQNcXV1hZGSEAwcOoH///sJjNm3ahIULF2LPnj1o0aIFkpOT4evrC2dnZ/Tq1Qva2tpYsmQJcnJqr91oa2uLW7duYd26dbC3t0dOTg709fUxYMAArF27tta/W9X/898Gynsl7Nq1C6tXr8ZPP/2EoUOHws3NDTt2fHrYO3ToUJw9exaenp5Yv349ZGVl0bZtW0yfPh1AecNlHx8fLFy4EHw+HxYWFjhz5gyaNGlS7/z/r5Z2PQAeD6knb1W7X6lFE5EZV9IKsrBYMgEqLZuitOAjXodE4M68nSjJ+TS78PHPx/ExKwdmrjbo0lIHJTn5eB+djNhtp2vMUZJbiNCJPrBcNw2DL6xB8Yd8PPU7j3i/YOExUrIy+NrDHorNtMAvKkbO05e47rAer6/U3HCWlTd/3oKMpgoMFo6DnI4m8uNe4LGDNz5WrJKQ09WE/GdNrTqHfKq9q/q1CXTGfoOiF5m413VOg2b/L53ZdRJyCvJwXvsdlNVU8CziKbwnr0JRlZk82s2birzm5JXk4bT2OzTRa4LiomK8epaGXxb44s7ZT7PDBzsOAwB4HPvUtwMAdi7ahut/XPmPf6v/T/qftyGrqQLThWMhr6uB3LgXeGjvg6KK14i8jiYUq7xGClPf4KH9z2jrOQUtnYagKOM9Ypf7I+Pcp1k5Ra/e4sFEb7T1nILeV3/Gx/T3SNlzAUnb/xQe09JpCACg+ynRPgLRrjuRdjT0v/yV/7GSu9fAU1GDgp0jeBpaKHuZjPyNyyB4W74yREqjCaSa/L0bSJ5mE6h67Rb+LD9iIuRHTERpbATyvRf9q/kb0o1dZyCrIAebNU5QUFfGy4hn8HdcJzKLV6NFEwgEnx6IqOpqYm7wOuHP33w3Et98NxLP78Rg36Tyz9OzKwMwaNF42KxxgrK2OnIz3uP+7yG4uq36RtZcdmvXWcgqyGH42mlQVFNGWsQzHJrsI3KO1JuLfgaq6mriu/Pewp97fTcSvb4bieTbMQicVH79UW2mhTHb50JJUxX573KQ9igR+0avbFwzBivs2R4ABQV5rFy/FOrqqogMfwzn8XORn//pM1/vq2Yoq/o6UlfFmk3L0VSnCXJz8hDzOB4ONjMQ9egJi1/hXxW+8yxkFOTQb+00yKsrISPiGf50+FlklrNqC22R1TTpDxNwcc4O9Fg8Ht3dx+FDSgYufr9DpKQMAOh/0w5qX2kjtobrsLKOBvp4OEBJWx35mdmIPxGG+1vFV8lyze1d5efMeu00KFS8zw7X4302vcr7rOd3I9Hzu5FIuR2DQ5NEP+cbI5avIwBobVN+75/wJ3cbxD+qOEd9q5yjM5+dI5VqztGlOTvQffF4dHMfh5yUDFz+fgcyqzlHqjWcI0FZGTRN9NBm93woaqqiKDsPmZFJODVuLd4/rbn+PmulD0JRpKwK+REO4KlroexVCgp2/ATBu/L7I566FqS0mv6tf5OnqALFyfPLyzQWFqDsRSIKNrqjLJnbq95qc7Pic39Exef+y4hnOFiP69GsKtej3t+NRO+Kz33/iuvRq6gkHJ25BQOXTEQ/19F4//INLqw+hOhT1X+/5qq889eRqaGGJt87QLqpJooTUpA2awVKX5W/jqSbakFG79N9tvrE4eDJykDXYy50PeYKt384eRkZP24CAGh8OwpScnJovk20KfzbHYfw9pdDDfBb/buGtm2O7MJi+N1KQFb+R5hqq2DH2K5orq4EAHiT9xGvK1b0AYCSnAx2je8On5AncDgYBnVFOQxpo4c5fdoIj8n9WIo1l6KRlf8RKvIyaKujhn2TesJCT6Ohf70G9zguAc7zPq38W7+9/DuZ7bBB8Pqp8X4PI+TfwBP80053hJkZM2YgLi4ON2403KzAo3oODfbfaqz0BNycMc4lO+W5PVOcC6YWVb/Uk3zSc0gjKUnE0IYb1S8zJuXkBNwuR8QFhwoa74OZhuKq0JZ1BM77IEVfN+qiXkbXo7pIsQ7QCEweTvdGddl8QbvugyScvRJ3e6Zxhf4ic9YROE/Gbm7dB0k4WW1j1hEaHUXF6iudNITCwpS6D2pkGvVKAUmzceNGDB48GMrKyjh//jwCAgLw66+/so5FCCGEEEIIIYQQQgghpJGgQYFG5N69e1i/fj1yc3NhbGyMbdu2CUsDEUIIIYQQQgghhBBCyJeIit38u2hQoBE5duwY6wiEEEIIIYQQQgghhBBCGjEqzUgIIYQQQgghhBBCCCGESAhaKUAIIYQQQgghhBBCCCGEswSg8kH/JlopQAghhBBCCCGEEEIIIYRICFopQAghhBBCCCGEEEIIIYSzqNHwv4tWChBCCCGEEEIIIYQQQgghEoJWChBCCCGEEEIIIYQQQgjhLFop8O+ilQKEEEIIIYQQQgghhBBCiISgQQFCCCGEEEIIIYQQQgghREJQ+SBCCCGEEEIIIYQQQgghnEXFg/5dtFKAEEIIIYQQQgghhBBCCJEQPAF1aSCN1MePH7Fu3TosW7YM8vLyrONwEp2jutE5qhudo7rROaobnaO60TmqG52jutE5qhudo7rROaobnaO60TmqG52j2tH5qRudo7rROSKkejQoQBqtnJwcqKur48OHD1BTU2Mdh5PoHNWNzlHd6BzVjc5R3egc1Y3OUd3oHNWNzlHd6BzVjc5R3egc1Y3OUd3oHNWOzk/d6BzVjc4RIdWj8kGEEEIIIYQQQgghhBBCiISgQQFCCCGEEEIIIYQQQgghRELQoAAhhBBCCCGEEEIIIYQQIiFoUIA0WvLy8li5ciU1iqkFnaO60TmqG52jutE5qhudo7rROaobnaO60TmqG52jutE5qhudo7rROaobnaPa0fmpG52jutE5IqR61GiYEEIIIYQQQgghhBBCCJEQtFKAEEIIIYQQQgghhBBCCJEQNChACCGEEEIIIYQQQgghhEgIGhQghBBCCCGEEEIIIYQQQiQEDQoQQgghhBBCCCGEEEIIIRKCBgVIo8Hn8xEaGor379+zjkIIIYQQQhqJAQMGIDs7W2x7Tk4OBgwY0PCBCCGEENJg+Hw+IiIi6FkSIZ+hQQHSaEhLS2Po0KHVfqkjhBBCGqP8/HzWERoVgUAAgUDAOgZpZK5du4bi4mKx7UVFRbhx4waDRNxiZ2eHs2fPoqysjHUUzissLERBQYHw55SUFGzZsgWXLl1imIo7nJ2dkZubK7Y9Pz8fzs7ODBIRQiTRggULsG/fPgDlAwL9+vWDpaUl9PX1ce3aNbbhCOEQGhQgjYqFhQWSkpJYx+C8kJAQ/Pjjj5g+fTqcnZ1F/keAkpISWFlZ4enTp6yjcBp98a3dixcv8PLlS+HP9+7dw4IFC7B7926GqbgnOzsbe/fuxbJly/Du3TsAQHh4ONLS0hgn4wZdXV04OzsjLCyMdRRO27dvH9q3bw8FBQUoKCigffv22Lt3L+tYhOOioqIQFRUFAIiJiRH+HBUVhUePHmHfvn1o0aIF45TsFRYWws7ODl999RV+/PFHJCQksI7EWba2tggMDARQ/vnWvXt3bNq0Cba2tti5cyfjdOwFBASgsLBQbHthYaHwvBFC/js0gbLcH3/8ga+//hoAcObMGTx//hxxcXFYsGABli9fzjgdIdwhwzoAIX+Hl5cX3N3dsWbNGnTu3BnKysoi+9XU1Bgl447Vq1fD09MTXbp0gZ6eHng8HutInCMrK4vHjx/TuamDra0txowZg1mzZgm/+MrKyiIrKwubN2/G7NmzWUdkyt7eHjNnzoSjoyPS09MxePBgtGvXDocOHUJ6ejo8PDxYR2QuKioKgwYNgrq6OpKTkzFjxgxoaWnh5MmTSElJoQcEAA4fPgx/f38MHDgQBgYGcHZ2xpQpU9C8eXPW0ThjxYoV8PX1xbx589CzZ08AwO3bt+Hm5obk5GSsXbuWcUL28vPz4ePjg5CQEGRmZorN+JbUCRUdO3YEj8cDj8ertkyQoqIitm/fziAZt1y8eBEvX77EgQMHEBAQgJ9//hm9e/fG9OnTMX78eCgqKrKOyBnh4eHw9fUFUP7QSVdXF48ePcKJEyfg4eEhsfdGOTk5wpVcubm5UFBQEO7j8/kIDg6Gjo4Ow4TcMnr06Gq/h/B4PCgoKMDU1BT29vZo06YNg3TsdOrUqd7fz8LDw//jNNz3888/w9DQEBMnTgQATJgwASdOnECzZs0QHBwsfCguibKystCsWTMAQHBwMMaPH4/WrVvDxcUF27ZtY5yOEO7gCWgNNmlEpKQ+LW6pesMgEAjA4/HA5/NZxOIUPT09rF+/Ho6OjqyjcNqiRYsgKysLHx8f1lE4S1tbG6GhoWjXrh327t2L7du3i3zxjY2NZR2RKU1NTdy5cwdt2rTBtm3bcPToUdy8eROXLl3CrFmzJPYhXFWDBg2CpaUl1q9fD1VVVURGRsLY2Bi3bt2Cvb09kpOTWUfkjLdv3yIwMBD+/v6IiYnB0KFD4ezsDBsbG8jISPYcDm1tbWzfvh3ffvutyPbDhw9j3rx5yMrKYpSMO7799luEhobC0dGx2gkB8+fPZ5SMrZSUFAgEAhgbG+PevXto2rSpcJ+cnBx0dHQgLS3NMCE3Xb16Ffv378fJkychLS2NSZMmwdnZGd27d2cdjTklJSXExcWhZcuWmDBhAtq1a4eVK1fixYsXaNOmjcgKS0kiJSVV68NcHo+H1atX0wzdCtOmTcOpU6egoaGBzp07QyAQ4NGjR8jOzsaQIUMQGRmJ5ORkhISEoHfv3qzjNpjVq1cL/1xUVIRff/0V5ubmwgkBd+7cwZMnT/D9999j3bp1rGJyhrGxMQ4dOoRevXrh8uXLmDBhAo4ePYpjx44hNTVVold3GxgYYM+ePRg4cCCMjIzw66+/YuTIkXjy5An69OlDvQUIqSDZ3zJJo3P16lXWETivuLgYvXr1Yh2D84qLi7F3715cvnwZXbp0EVt1snnzZkbJuKOgoACqqqoAgEuXLmHMmDGQkpJCjx49kJKSwjgdeyUlJZCXlwcA/PXXX7CxsQEAtG3bFq9fv2YZjTPu378PPz8/se0tWrRAeno6g0Tc1aRJE7i5ucHNzQ3bt2/H4sWLERwcDG1tbcyaNQtLly6FkpIS65hM8Pl8dOnSRWx7586dUVpayiAR95w/fx7nzp2TqIdH9WFgYICSkhJMmTIFWlpaMDAwYB2pUbCysoKVlRVyc3Px+++/48cff8S+ffvo/QbA1NQUp06dwujRo3Hx4kW4ubkBADIzMyV6xfLVq1chEAgwYMAAnDhxAlpaWsJ9cnJyMDAwoBVwVTRr1gz29vbYsWOHcNJbWVkZ5s+fD1VVVRw5cgSzZs3CkiVLJKq84MqVK4V/nj59OlxdXbFmzRqxY168eNHQ0Tjp9evX0NfXBwCcPXsWEyZMwJAhQ2BoaCjxg7hOTk6YMGGCcKLE4MGDAQB3795F27ZtGacjhDtoUIA0Kv369WMdgfOmT5+O33//HStWrGAdhdMeP34MS0tLABDrLUBlhcrRF9/atWvXDrt27cKIESNw+fJl4ZeWV69eoUmTJozTcYOCggJycnLEtsfHx4vM2CVAeno6AgMDceDAAaSmpmLcuHFwcXHBq1ev4OPjgzt37kjsjK/Jkydj586dYoO1u3fvhoODA6NU3KKpqSnyEI58Iisriz///JNKuv1NSUlJ8Pf3h7+/Pz58+IBBgwaxjsQJHh4esLe3h5ubGwYMGCCcwXzp0iV06tSJcTp2Kr+jPX/+HC1btqR76Trs27cPN2/eFFkFLyUlhXnz5qFXr17w9vbG3Llz8c033zBMydbx48fx4MEDse2TJ09Gly5dsH//fgapuEVTUxMvXryAvr4+Lly4ICynKBAIJL6CwqpVq9C+fXu8ePEC48ePF07kkpaWxtKlSxmnI4Q7aFCANDo3btyAn58fkpKScPz4cbRo0QIHDx6EkZER+vTpwzoec0VFRdi9ezf++usvdOjQAbKysiL7aQZ8OVp1UreqX3wHDhxIX3w/8/PPP2P06NHYsGEDpk6dKqzbefr0aXTr1o1xOm6wtbWFp6cnjh07BqB8wC01NRVLly7F2LFjGafjhqCgIBw4cAAXL16Eubk55syZg8mTJ0NDQ0N4TMeOHSX+Pbdv3z5cunQJPXr0AFBeQuDFixeYMmUKFi5cKDxOUj/j1qxZAw8PDwQEBEjsipLa2NnZ4dSpUyKvFSKusLAQx48fx4EDB3D9+nW0bNkS06dPh5OTk3A2qqQbN24c+vTpg9evX4vU6x44cCBGjx7NMBk3XLlyBSoqKhg/frzI9uPHj6OgoABTp05llIxbSktLERcXh9atW4tsj4uLEz7MVVBQkOjBFUVFRYSFhaFVq1Yi28PCwkR6VkiyMWPGwN7eHq1atcLbt28xbNgwAEBERARMTU0Zp2Nv3LhxAMqfj1SiaxAhomhQgDQqJ06cgKOjIxwcHBAeHo6PHz8CAHJzc+Ht7Y3g4GDGCdmLiopCx44dAZTPhq9Kkm8sa5KYmIhnz56hb9++UFRUFPanILV/8R0zZgzDZNzQv39/ZGVlIScnB5qamsLtM2fOpIdyFTZu3Ijhw4dDR0cHhYWF6NevH9LT09GzZ094eXmxjscJTk5OmDRpEm7evImuXbtWe4yxsbFE12GuurLr2bNnAICmTZuiadOmIp9zknzt3rRpE549ewZdXV0YGhqKTQiQ9IaMpqamWLNmDW7duoXOnTuLlQx0dXVllIwbbt26hQMHDuDYsWMoLi6GnZ0dLl68SKsDatCsWTPk5eXh8uXLwvvHrl27SvQ1qJKPjw927doltl1HRwczZ86kB3IVHB0d4eLigh9//FH42rl37x68vb0xZcoUABD29ZJUCxYswOzZs/Hw4UORCQH79++nlV8VfH19YWhoiBcvXmD9+vVQUVEBUF5W6Pvvv2ecji0+nw9vb2/s2rULGRkZePr0KYyNjbFixQoYGhrCxcWFdURCOIEaDZNGpVOnTnBzc8OUKVNEmlZGRETA2tqaalSTenv79i0mTJiAq1evgsfjISEhAcbGxnBxcYGGhgY2bdrEOiJzzs7O2Lp1q7CvQKX8/HzMmzePlu2ifKbXtWvX8OzZM9jb20NVVRWvXr2Cmpqa8MaclM8cDA8PR1lZGSwtLelBUxUFBQU0iET+sarNGatTtU6zJDIyMqpxH4/Hk/jG8FJSUvj666/h4uICBwcHkYFuIoruH2unoKCAuLg4GBoaimxPTk6GmZkZCgsL2QTjGD6fDx8fH+zYsQMZGRkAAF1dXcybNw9LliyBtLQ0UlNTISUlha+++opxWnaOHTuGrVu3IjY2FgBgZmaG+fPnY8KECYyTEa7z9PREQEAAPD09MWPGDDx+/BjGxsY4duwYfH19cfv2bdYRCeEEGhQgjYqSkhJiYmJgaGgoMiiQlJQEc3NzkaVhko5mwNduypQpyMzMxN69e2FmZiZ8LV26dAlubm548uQJ64jMSUtL4/Xr19DR0RHZnpWVhWbNmkl8w8GUlBRYW1sjNTUVHz9+FM5AWbBgAYqKiqqdKUfI52p6n719+xY6OjoSXxOWEPLfs7GxwZEjR2iAsh7o/rF2LVu2xI4dO2BjYyOy/c8//8ScOXPw8uVLRsm4q7L3EvXrIv+PgwcPCksr3759GwYGBtiyZQuMjIxga2vLOh4zpqam8PPzw8CBA0WeG8XFxaFnz554//4964iEcAKVDyKNip6eHhITE8Vmn4SFhcHY2JhNKI6paQbT9OnTaQZTFZcuXcLFixfFZt+0atUKKSkpjFJxQ05ODgQCAQQCAXJzc0XqdvL5fAQHB4s9wJRE8+fPR5cuXRAZGSnSWHj06NGYPn06w2Tc4erqClNTU7HSHDt27EBiYiK2bNnCJhiH1DQ34+PHj5CTk2vgNNxkZWVV66D2lStXGjANtz18+BCxsbHg8XgwNzeX+F4U1al8z9FEiU/OnTuHvLw8GhSoB7p/rN2kSZPg6uoKVVVV9O3bF0B5GZz58+dj0qRJjNNxEw0G1I4+12q2c+dOeHh4YMGCBfDy8hJOJNHQ0MCWLVskelAgLS2t2r4KZWVlKCkpYZCIEG6iQQHSqHz33XeYP38+9u/fDx6Ph1evXuH27dtwd3en2oIV3NzcICsri9TUVJiZmQm3T5w4EW5ubjQoUCE/P7/aL79ZWVmQl5dnkIg7NDQ0wOPxwOPxxBqgAeUPUuoqVSEJwsLCcPPmTbEHtwYGBkhLS2OUiltOnDiB06dPi23v1asXfHx8JHpQYNu2bQDK30979+4VKTfF5/Nx/fp1tG3bllU8Tqnsk1OppKQEERERePz4MdWnrpCZmYlJkybh2rVr0NDQgEAgwIcPH2BlZYUjR46gadOmrCMyFxgYiA0bNiAhIQEA0Lp1ayxevBiOjo6Mk7FHC8frj+4fa7d27VqkpKRg4MCBkJEpf9RQVlaGKVOmwNvbm3E67sjIyIC7uztCQkKQmZkp9h6kVYL0uVYf27dvx549e2BnZwcfHx/h9i5dusDd3Z1hMvbatWuHGzduwMDAQGT78ePHaWCJkCpoUIA0Kj/88IPwZqCoqAh9+/aFvLw83N3dMXfuXNbxOIFmMNVP3759ERgYiDVr1gAofzBXVlaGDRs2wMrKinE6tq5evQqBQIABAwbgxIkT0NLSEu6Tk5ODgYEBmjdvzjAhN5SVlVX7pe3ly5difRgk1du3b6Guri62XU1NDVlZWQwScYevry+A8odxu3btgrS0tHCfnJwcDA0NqQRVhcpz9blVq1YhLy+vgdNw07x585CTk4MnT54IJwTExMRg6tSpcHV1xeHDhxknZGvz5s1YsWIF5s6di969e0MgEODmzZuYNWsWsrKy4Obmxjoic7Ryon7o/rF2cnJyOHr0KNasWYPIyEgoKirCwsJC7MGcpJs2bRpSU1OxYsUK6Onp0fuvGvS5Vrfnz59X+4BbXl4e+fn5DBJxx8qVK+Ho6Ii0tDSUlZUhKCgI8fHxCAwMxNmzZ1nHI4QzqKcAaZQKCgoQExODsrIymJubU0PPKlRVVREeHo5WrVqJ1M+7f/8+rK2t8fbtW9YROSEmJgb9+/dH586dceXKFdjY2ODJkyd49+4dbt68CRMTE9YRmUtJSYG+vj6kpKRYR+GkiRMnQl1dHbt374aqqiqioqLQtGlT2NraomXLljhw4ADriMy1b98es2bNEhu03b59O3bu3ImYmBhGybjDysoKQUFB1Njz/5CYmIhu3brh3bt3rKMwp66ujr/++gtdu3YV2X7v3j0MGTIE2dnZbIJxhJGREVavXo0pU6aIbA8ICMCqVavw/PlzRsm4QUpKCu3btxfO7K5JeHh4AyXiLrp/JP8GVVVV3LhxQ2wlHPmEPtfqZm5ujnXr1sHW1lbke/+2bdsQEBCAhw8fso7I1MWLF+Ht7Y2HDx+irKwMlpaW8PDwwJAhQ1hHI4QzaKUAaZSUlJTQpUsX1jE4iWYw1Y+5uTmioqKwc+dOSEtLIz8/H2PGjMGcOXOgp6fHOh4nGBgYIDs7G/fu3UNmZibKyspE9n/+cEXS+Pr6wsrKStjk3N7eHgkJCdDW1qbZSxUWLlyIuXPn4s2bNxgwYAAAICQkBJs2bZLo0kFVXb16lXWERuv27dsiPU8kWVlZGWRlZcW2y8rKil27JdHr16/Rq1cvse29evXC69evGSTinqFDh9Ikm3qg+8e6vXz5EqdPn0ZqaiqKi4tF9m3evJlRKm7R19ensl11oM+1ui1evBhz5sxBUVERBAIB7t27h8OHD2PdunXYu3cv63jMDR06FEOHDmUdgxBOo5UChPPGjBlT72ODgoL+wySNA81gIv+WM2fOwMHBAfn5+VBVVRVZ2szj8Wh2LoDCwkIcPnwY4eHhwhkoDg4OUFRUZB2NM3bu3AkvLy+8evUKAGBoaIhVq1ZJ9KDSwoULsWbNGigrK2PhwoW1HksPUMTvAwQCAV6/fo0HDx5gxYoVWLlyJaNk3GFra4vs7GwcPnxYWN4tLS0NDg4O0NTUxMmTJxknZKt9+/awt7fHjz/+KLJ97dq1OHr0KKKjoxkl4wYpKSmkp6dDR0eHdRTSyIWEhMDGxgZGRkaIj49H+/btkZycDIFAAEtLS2oMX+HSpUvYtGkT/Pz8YGhoyDoOJ9HnWv3s2bMHa9euxYsXLwAALVq0wKpVq+Di4sI4GSGkMaBBAcJ5Tk5Owj8LBAKcPHkS6urqwpUCDx8+RHZ2NsaMGUPlOiq8fv0au3btElkqRzOYxNEs+Nq1bt0aw4cPh7e3d7VN9Qj5O968eQNFRUWaiYrykkEnT56EhoYG+vfvX2MtYR6PRw9QIHofAJQ/wGzatCkGDBhAS8ArvHjxAra2tnj8+DH09fXB4/GQmpoKCwsL/Pnnn2J9hiTNiRMnMHHiRAwaNAi9e/cGj8dDWFgYQkJCcOzYMYwePZp1RKakpaXx+vVrGhSoQVRUFNq3bw8pKSlERUXVemyHDh0aKBU3devWDdbW1vD09BSWM9HR0YGDgwOsra0xe/Zs1hE5QVNTEwUFBSgtLYWSkpLYjHiaeEOfa39XVlYWysrK6DpeQVNTs9r7ax6PBwUFBZiammLatGli95iESBoaFCCNypIlS/Du3TuRpox8Ph/ff/891NTUsGHDBsYJSWNBs+DrpqysjOjoaBgbG7OOwllPnz7FtWvXqh1Y8vDwYJSKkC9LdnY2NDQ0qt2XmJgIU1PThg3EYZcvX0ZcXBwEAgHMzc0xaNAg1pE44+HDh/D19UVsbKzw/CxatKjaJo2ShlYK1K7q+ZGSkgKPx6u29AuPxwOfz2eQkDtUVVUREREBExMTaGpqIiwsDO3atUNkZCRsbW2RnJzMOiInBAQE1Lp/6tSpDZSE++hzjfw/fH194eXlhWHDhqFbt24QCAS4f/8+Lly4ADc3Nzx//hwHDx7E9u3bMWPGDNZxCWGGBgVIo9K0aVOEhYWhTZs2Itvj4+PRq1cvaqILwNjYGP369cOuXbsgLy8v3J6VlYVu3bohKSmJYTruoFnwdRszZgwmTZqECRMmsI7CSXv27MHs2bOhra2NZs2aiQ0sSWpDRktLS4SEhEBTUxOdOnWqcRY8QE0rS0tLoaCggIiICLRv3551HM7q1asXrly5ItY/ID4+HgMHDsTLly8ZJSPky5CSkoKWLVvWer2WZFXPT0pKSq3HGhgYNFAqbmrWrBmuXLkCc3NztGvXDuvWrYONjQ0iIyPRu3dv5OXlsY5IyBejpvvsz2fDS2JfwbFjx2Lw4MGYNWuWyHY/Pz9cunQJJ06cwPbt27F7926JLyFIJBs1GiaNSmlpKWJjY8UGBWJjY6nhUIXk5GTIyMjgm2++wZ9//iksGcTn8+v8IiNJ0tLS4OrqSgMCtRgxYgQWL16MmJgYWFhYiC1ttrGxYZSMG9auXQsvLy8sWbKEdRROsbW1FQ5I2tra0kOmWsjIyMDAwEDiZ5bWRVNTE3Z2djh79ixkZMpvXWNjYzFgwACJHrTctm0bZs6cCQUFBWzbtq3WY11dXRsoFXfx+XycPHkSsbGx4PF4MDMzg62trfA1JckqH2TX1OOk6gMmW1tbaGlpNWQ85qo+6G/atCndO9aiR48euHnzJszNzTFixAgsWrQI0dHRCAoKQo8ePVjHYyonJwdqamrCP9em8jhJFxISgpCQkGpX5O7fv59RKu6wtrbGzp07YWFhIZwN/+DBA0RFRWHatGmIiYnBoEGDEBQUBFtbW9ZxG9TFixfx888/i20fOHAgFi1aBAAYPnw4li5d2tDRCOEUWilAGpWFCxfC398fP/74o/DG8s6dO/Dx8cGUKVOoISPK68ImJCTA3d0dd+/exalTp9C1a1dkZGSgefPm9PCpAs2Cr5uUlFSN+2iJfPkXtoiICCqvRP6RAwcO4Pjx4zh06JDEPWirr6KiIgwePBh6eno4evQonjx5goEDB8LBwUGiP/eNjIzw4MEDNGnSBEZGRjUex+PxJH6V4OPHj2Fra4v09HThxJKnT5+iadOmOH36NCwsLBgn5AYrKyuEh4eDz+ejTZs2EAgESEhIgLS0NNq2bYv4+HhhPwZzc3PWcZlQUVGBnZ0dHB0dMXjw4FrvlSRRUlIS8vLy0KFDBxQUFMDd3R1hYWEwNTWFr6+vRK+kqNq7o7IM1ecEAgHdY1dYvXo1PD090aVLF+jp6YmdL2o0DMyYMQMtW7bEihUrRLavXbsWKSkp2LNnD1auXIlz587hwYMHjFKy0bJlS7i5ucHNzU1ku6+vL3x9fZGamoqoqCgMGTIE6enpjFISwh4NCpBGpaysDBs3bsTWrVvx+vVrAICenh7mz5+PRYsWCfsMSLKqdU+XLVuGrVu3Yvfu3Rg8eDANClSxb98+eHp6wsnJiWbBk/+Li4sLunbtKrYslXzi5OSEyZMnY8CAAbRioAadOnVCYmIiSkpKYGBgAGVlZZH9kl5iqdKHDx/Qv39/mJiY4MaNG5gyZQr1ESL11qNHD+jo6CAgIACampoAgPfv32PatGnIzMzE7du3GSfkhi1btuDGjRs4cOCAyIxmFxcX9OnTBzNmzIC9vT0KCwtx8eJFxmnZCAoKwuHDh3Hu3Dmoqalh4sSJmDx5Mrp27co6GnN8Ph9hYWHo0KGD8H1GPgkNDUXv3r0hIyOD0NDQWo/t169fA6XiLj09Paxfvx6Ojo6so3CWuro6Hj58KNZbKTExEZ07d8aHDx8QFxeHrl27Ijc3l1FKNirLvA4fPhzdunUDj8fDvXv3EBwcjF27dsHFxQWbNm3CvXv3cPToUdZxCWGGBgVIo1W57JKWV4qqOgsFAA4dOoQZM2bg22+/RUBAAA0KVKBZ8H9PUVGRWD1vSbdu3Tps3rwZI0aMqHZgicp1lA+uXbp0CU2aNMGkSZPg6OiIjh07so7FKatXr651/8qVKxsoCbdUV1ohPT0dgwYNwsiRI+Hj4yPcTvcBgKenJ9zd3cXKmhQWFmLDhg0S3/hcUVERDx48QLt27US2P378GF27dkVhYSGjZNzSokULXL58WWwVwJMnTzBkyBCkpaUhPDwcQ4YMQVZWFqOU3JCbm4s//vgDhw8fxtWrV2FkZITJkydL/HtNQUEBsbGxta5eIqQ+mjRpgnv37sHExIR1FM7S1dXFhg0bMGXKFJHtgYGBWLx4MTIyMhATE4O+fftK5DX75s2b2LFjB+Lj4yEQCNC2bVvMmzcPvXr1Yh2NEM6gQQFCvjBVVwpUun37NkaPHo03b97Qw25Sb3w+H97e3ti1axcyMjLw9OlTGBsbY8WKFTA0NISLiwvriExRuY76yc7OxrFjx/D777/jxo0baNOmDSZPngx7e3sYGhqyjkc4qrbSCkD5e4zKLHzy+YSASm/fvoWOjo7En6OOHTti8+bNGDBggMj2K1euYP78+dRksIKKigrOnj2L/v37i2y/du0aRo0ahdzcXCQlJaFjx4511kSXJDExMXBwcEBUVJTEv9e6du0KHx8fDBw4kHUUzomKiqr3sR06dPgPkzQOS5YsgYqKilhpHPLJ2rVr4e3tjRkzZqBr167C2fB79+7Fjz/+iOXLl8PX1xfBwcG4fPky67gNpqSkBDNnzsSKFSuozCshdaBBAdKoZGRkwN3dXdhw6POXr6TfiNcmIyMDcXFxtBy1QmBgICZOnChsiFqpuLgYR44cEZtxIYk8PT0REBAAT09PzJgxA48fP4axsTGOHTsGX19fKrdA/raXL1/i8OHD2L9/PxISElBaWso6EuGoukorVEWfa+WDKBkZGWjatKnI9itXrmDixIl48+YNo2TcEBwcjB9++AGrVq0S6Unl6ekJHx8f9OnTR3isJK88cXBwwO3bt7Fp0yaRB0zu7u7o1asXDh48iCNHjmDjxo0SV5/6c0VFRTh9+jR+//13XLhwATo6Ovj222+rbWwpSS5duoQlS5ZgzZo16Ny5s1hJPEl+f1UOdtf1+EWSB7urNjsvKytDQEAAOnTogA4dOoityJXknkJV/fbbb8LZ8ADQpk0bzJs3D/b29gDKVwxWNouXJBoaGggPD6dBAULqQIMCpFEZNmwYUlNTMXfu3GobDtna2jJKRhobmlVZN1NTU/j5+WHgwIFQVVVFZGQkjI2NERcXh549e+L9+/esI5JGpKSkBOfOncOhQ4dw7tw5aGlpIS0tjXUs5mqaEV+JrkWkNpqamuDxePjw4QPU1NREXkt8Ph95eXmYNWsWfvnlF4Yp2ataMrDyHFVddVL5syQ/jAOAvLw8uLm5ITAwUDhoKyMjg6lTp8LX1xfKysqIiIgAAIktBXfp0iX89ttvOHXqFKSlpTFu3Dg4ODjQ4GSF6t5rAL2/ACAlJaXex0pqQ2YrK6t6H3v16tX/MAn3lZaWwsvLC87OztDX12cdh3Mq+wZWHWgihIiTYR2AkL8jLCwMN27ckNgvIjWxtLRESEgINDU10alTp1ofMFHTynKVX04+9/LlS6irqzNIxD1paWlijauA8pk7JSUlDBKxt3DhQqxZswbKysp13mTSDKZyV69exe+//44TJ06Az+djzJgxOHPmjFgZD0l18uRJkZ9LSkrw6NEjBAQE1NlvQFIcOHAAKioqGD9+vMj248ePo6CgAFOnTmWUjL0tW7ZAIBDA2dkZq1evFvn8kpOTg6GhIXr27MkwITdI+sOj+lJRUcGePXvg6+uLpKQkCAQCmJiYQEVFRXiMpN+D29nZYcSIEQgICMCIESPEZi9LOnqv1UxSH/T/HfT6qT8ZGRls2LBBou+BamNqaoo1a9bg1q1b1a5aot5vhJSjQQHSqOjr69e55FIS2draCsvg2Nra1jooIOkqB014PB4GDhwIGZlPl0E+n4/nz5/D2tqaYULuaNeuHW7cuCH2Jeb48ePo1KkTo1RsPXr0SDgg8ujRI8ZpuO+rr77C27dvMXToUPj5+WHUqFESt3y5LtWtcBs3bhzatWuHo0ePSnzvDgDw8fHBrl27xLbr6Ohg5syZEv2FuPJ3NzIyQu/evUU+08gnNIv771FRUaGa5jVIT0+X6BI4dTEyMoK+vr7YdxGBQIAXL14wSsUNp0+frvexNjY2/2GSxsHZ2Rlbt26FqqqqyPb8/HzMmzcP+/fvZ5SMOwYNGoRr165h2rRprKNwzt69e6GhoYGHDx/i4cOHIvt4PB4NChBSgcoHkUbl0qVL2LRpE/z8/KhBJfm/VM68Xb16NRYtWiQy+61yVuXYsWMhJyfHKiJnnDlzBo6Ojli2bBk8PT2xevVqxMfHIzAwEGfPnsXgwYNZRyQct3v3bowbNw5aWlqsozQ6z549Q4cOHZCfn886CnMKCgqIi4sT+9xPTk6GmZkZCgsL2QTjkODgYEhLS2Po0KEi2y9evIiysjIMGzaMUTLueP/+Pfbt24fY2FjweDyYmZnBycmJrk9V5Ofnw8fHR9i7q6ysTGR/UlISo2TcwufzcerUKZHXkq2tLaSlpVlHY47Kc9asamml2kh6maVKNb2WsrKy0KxZM+pLBcDPzw+rVq2Cg4NDtbPhaXCJEFIXmk5EGpWJEyeioKAAJiYmUFJSEluy++7dO0bJuMPY2Bj3799HkyZNRLZnZ2fD0tJS4r/QrVy5Enw+HwYGBhg6dCj09PRYR+KsUaNG4ejRo/D29gaPx4OHhwcsLS1x5swZGhCoRWxsLEaMGCHx77XS0lJ8//336NWrFz10+5sKCwuxfft2fPXVV6yjcIKOjg6ioqLEBgUiIyPFPusk1dKlS+Hj4yO2XSAQYOnSpRI/KBAaGgobGxuoq6ujS5cuAIBt27bB09MTp0+fppUEFaZPn47Q0FA4OjpW27uLAImJiRg+fDjS0tLQpk0bCAQCPH36FPr6+jh37hxMTExYR2SqpvKceXl5Er9S8PNBNlK9nJwcCAQCCAQC5Obmirxu+Hw+goODxQYKJNXs2bMBVF+ylAaXCCH1QYMCpFHZsmUL6wicl5ycXO0NwMePH/Hy5UsGibhHWloas2bNQmxsLOsonDd06FCxmaekdsXFxX+rmdyXSkZGBgYGBvSFpA6VjWIrVX4JVlJSwqFDhxgm445JkybB1dUVqqqq6Nu3L4Dyh7zz58/HpEmTGKfjhoSEBJibm4ttb9u2LRITExkk4pY5c+Zg4sSJ2Llzp3A2N5/Px/fff485c+bg8ePHjBNyw/nz53Hu3Dn07t2bdRTOcnV1hYmJCe7cuSMc8H779i0mT54MV1dXnDt3jnFCNir7LPF4PKxYsQJKSkrCfXw+H3fv3pX4fhSkfjQ0NISlXlu3bi22n8fjUc+lCjTQVLuXL1/i9OnTSE1NRXFxscg+6v1GSDkaFCCNiiTXDa5L1TqVFy9eFGk2yOfzERISAiMjIxbROMnCwgJJSUl0Tgj5D/30009YtmwZDh06RKsFauDr6ysyKCAlJYWmTZuie/fu0NTUZJiMO9auXYuUlBSRPjBlZWWYMmUKvL29GafjBnV1dSQlJYmtpkhMTBQrJyCJnj17hhMnToiUd5GWlsbChQsRGBjIMBm3aGpq0rW6DqGhoSIDAgDQpEkT+Pj4SPRgSmWfJYFAgOjoaJEynHJycvj666/h7u7OKh4nbNu2DTNnzoSCggK2bdtW67GSXO/86tWrEAgEGDBgAE6cOCHyXpOTk4OBgQGaN2/OMCFpDEJCQmBjYwMjIyPEx8ejffv2SE5OhkAggKWlJet4hHAG9RQgjc6zZ89w4MABPHv2DFu3boWOjg4uXLgAfX19tGvXjnU8ZirrVPJ4PLFmzLKysjA0NMSmTZswcuRIFvE459KlS1iyZAnWrFlTbQ1GSW0i9/ms5dpQua7qRUZGwtLSkmbIo7yxd2JiIkpKSmBgYCD2PgsPD2eUjDRGT58+RWRkJBQVFWFhYSHWBF2SzZw5E3fu3MHJkyeF5UsSExMxduxYdO3aFXv37mWckK3evXtj8eLFsLOzE9l+6tQp/Pzzz7h9+zabYBxz6NAh/PnnnwgICBCZ6U0+0dLSwtmzZ9GrVy+R7Tdv3sSoUaMk/t7IyckJW7duldj76NoYGRnhwYMHaNKkSa2Tkng8nsSXoASAlJQUtGzZksqY1SE/Px+hoaHVzoaX5MGlbt26wdraGp6enlBVVUVkZCR0dHTg4OAAa2trYeklQiQdDQqQRiU0NBTDhg1D7969cf36dcTGxsLY2Bjr16/HvXv38Mcff7COyJyRkRHu378PbW1t1lE4rWqzr89Ld0hyDcaAgADhn9++fYu1a9di6NCh6NmzJwDg9u3buHjxIlasWAE3NzdWMTmNBgU+qWt598qVKxsoCXcdOHAAKioqGD9+vMj248ePo6CggFbIkXr58OEDrK2t8eDBA2EvipcvX+Kbb75BUFAQNDQ02AZk7OjRo/jhhx8wb9489OjRAwBw584d/PLLL/Dx8YGZmZnw2A4dOrCKyVynTp3w7NkzCAQCGBoaivXuooFcYMqUKQgPD8e+ffvQrVs3AMDdu3cxY8YMdO7cGf7+/mwDEvIFuXHjBvz8/JCUlITjx4+jRYsWOHjwIIyMjNCnTx/W8Zh79OgRhg8fjoKCAuTn50NLSwtZWVlQUlKCjo6ORA8uqaqqIiIiAiYmJtDU1ERYWBjatWuHyMhI2NraIjk5mXVEQjiBBgVIo9KzZ0+MHz8eCxcuFI74VjbWtbOzQ1paGuuIpJEIDQ2tdT81HQTGjh0LKysrzJ07V2T7jh078Ndff+HUqVNsgjFW12qK0tJS5Ofn06AAqZc2bdpg165dsLKyEtkeGhqKmTNnIj4+nlEybqG6sHUTCAS4fPmycDVFhw4dhD0YJF3ViQDVqVxlKcmTAgAayK2P7OxsTJ06FWfOnBEOmpSWlsLGxgb+/v4i5TslUX5+Pnx8fBASEoLMzEyxmueS/JCS/D0nTpyAo6MjHBwccPDgQcTExMDY2Bi//vorzp49i+DgYNYRmevfvz9at26NnTt3QkNDA5GRkZCVlcXkyZMxf/58jBkzhnVEZpo1a4YrV67A3Nwc7dq1w7p162BjY4PIyEj07t0beXl5rCMSwgk0KEAaFRUVFURHR8PIyEhkUCA5ORlt27ZFUVER64hMUI1K8l9QUVFBREQETE1NRbYnJCSgU6dOEnszVXU1RW1ohne57Oxs/PHHH3j27BkWL14MLS0thIeHQ1dXFy1atGAdjzkFBQXExcWJ1YJPTk6GmZkZCgsL2QTjkLrqwl65coV1RE4pKiqCvLw8lVyo4u80f6eyVKQ+EhISEBcXB4FAAHNzc7F7JUn17bffIjQ0FI6OjtDT0xO7Ds2fP59RMu5ISEhAVFQULC0tYWRkhHPnzuHnn39GYWEh7Ozs8OOPP9L1G+Url9zc3DBlyhSR7/0RERGwtrZGeno664jMaWho4O7du2jTpg00NDRw+/ZtmJmZ4e7du5g6dSri4uJYR2TGzs4OI0aMwIwZM/DDDz/g5MmTmDZtGoKCgqCpqYm//vqLdURCOIEaDZNGRUNDA69fvxarw/jo0SOJfrjk6+sLBwcHKCgowNfXt8bjeDweDQp8pqCgoNqZp5JcPqBSkyZNcPLkSSxevFhk+6lTp9CkSRNGqdijh/31FxUVhUGDBkFdXR3JycmYMWMGtLS0cPLkSaSkpFCDTwA6OjqIiooSGxSIjIyU6PdZVcuWLcOiRYuEdWFPnDghUheWlDde9vLywq5du5CRkYGnT5/C2NgYK1asgKGhIVxcXFhHZIoe9JN/W6tWrdCqVSvWMTjn/PnzOHfunEQ3Xa7NyZMnMWHCBEhJSYHH42H37t2YOXMmrKysoKamhlWrVkFGRgZLlixhHZW5+Pj4ale7qampITs7u+EDcZCsrKxwAElXVxepqakwMzODuro6UlNTGadja/PmzcIJbKtWrUJeXh6OHj0KU1PTWp+XECJpaFCANCr29vZYsmQJjh8/Dh6Ph7KyMty8eRPu7u6YMmUK63jMPH/+vNo/k5q9efMGTk5OOH/+fLX7Jbl8QKXVq1fDxcUF165dE/YUuHPnDi5cuCDxTSsr0Sz42i1cuBDTpk3D+vXroaqqKtw+bNgw2NvbM0zGHZMmTYKrqytUVVWFX35DQ0Mxf/58TJo0iXE6boiNjcXhw4cBADIyMigsLISKigo8PT1ha2tLzeIArF27FgEBAVi/fj1mzJgh3G5hYQFfX1+JHxSoawBSku8htbS08PTpU2hra9dZHk+Sm+h6enrW6zgPD4//OAm3aWpqQktLi3UMzvLy8sIPP/yAtWvXwt/fH7NmzYKPjw8WLFgAANi9ezd8fX1pUACAnp4eEhMTxSZNhIWFwdjYmE0ojunUqRMePHiA1q1bw8rKCh4eHsjKysLBgwdhYWHBOh5TVV8jSkpK+PXXXxmmIYS7qHwQaVRKSkowbdo0HDlyBAKBADIyMigtLYWDgwP8/f0hLS3NOiJTOTk5UFFREaudW1ZWhry8PKipqTFKxj0ODg5ITk7Gli1bYGVlhZMnTyIjIwNr167Fpk2bMGLECNYROeHu3bvYtm0bYmNjhUvkXV1d0b17d9bRmPt8Fnx8fLxwZi7Ngi+nrq6O8PBwmJiYiCz9TklJQZs2bSS25FtVxcXFcHR0xPHjxyEjUz5Xo6ysDFOmTMGuXbsgJyfHOCF7VBe2bqampvDz88PAgQNF3mtxcXHo2bMn3r9/zzoiU5qamiI/l5SUoKCgAHJyclBSUpLoh90BAQGYNGkS5OXl6yyPJ8kr5aSkpNC8eXPo6Oigpq/PPB5P4psxHzp0CH/++ScCAgKgpKTEOg7nVG1+WlZWBjk5OURERKB9+/YAyksHmpubo6CggHFS9tavX4+AgADs378fgwcPRnBwMFJSUuDm5gYPDw+xnmeS6MGDB8jNzYWVlRXevHmDqVOnIiwsDKampjhw4AC+/vpr1hGZKy4urra/ScuWLRklIoRbaKUAaVRkZWXx22+/Yc2aNQgPD0dZWRk6depEy3dRvhx1yZIliIiIELsJLyoqQteuXbFx40aMGjWKUUJuuXLlCv7880907doVUlJSMDAwwODBg6GmpoZ169bRoECF7t2747fffmMdg5NoFnzdFBQUkJOTI7Y9Pj4eTZs2ZZCIe+Tk5HD06FGsWbNG2CDWwsKCyp1U0aNHD9y8eRPm5uYYMWIEFi1ahOjoaAQFBaFHjx6s43FCWlpatTXNy8rKUFJSwiARt1Q3KJKQkIDZs2eLlciTNFUf9EvyQ/+6WFtb4+rVq+jSpQucnZ0xYsQIiZ+MVJ1Nmzbh2bNn0NXVhaGhobAZcyVJHzTJz88X3jNKSUlBUVFR5HuboqIiPn78yCoep/zwww/48OEDrKysUFRUhL59+0JeXh7u7u40IFChS5cuwj83bdqUmi9X8fTpU7i4uODWrVsi2wUCAXg8HlUFIKQCDQqQRmXhwoVi2+7cuQMejwcFBQWYmprC1tZWIpet7ty5Ez/88EO1s3KUlJSwZMkS7NixgwYFKuTn50NHRwdA+dL5N2/eoHXr1rCwsJD4LyxVlZWVITExsdoZFtXV+ZQk9+/fh5+fn9j2Fi1aUPOzCra2tvD09MSxY8cAlM+iTE1NxdKlSzF27FjG6bjF0NAQAoEAJiYmwhUDpBzVha1bu3btcOPGDbHBpOPHj6NTp06MUnFbq1at4OPjg8mTJ0t0M8bqZGZmVvu5L8n9loKDg/H69Wv4+/tj8eLF+O677zBlyhQ4OzujTZs2rONxhp2dHesInMbj8URKdH3+MxHl5eWF5cuXIyYmBmVlZTA3N4eKigrrWKQRcHJygoyMDM6ePVtt03NCSDkqH0QaFSsrK4SHh4PP56NNmzYQCARISEiAtLQ02rZti/j4ePB4PISFhcHc3Jx13AbVvHlzXL9+vdqZggCQmJiIvn374tWrVw2cjJu6du2KtWvXYujQobCzsxOuENi2bZuwRryku3PnDuzt7ZGSkiK2VJ5mWJQ39Lpw4QI6deokUq7j0qVLcHFxwYsXL1hHZC4nJwfDhw/HkydPkJubi+bNmyM9PR09e/ZEcHAwlJWVWUdkrqCgAPPmzROW7ahsEOvq6ormzZtj6dKljBOSxuDMmTNwdHTEsmXL4OnpidWrVyM+Ph6BgYE4e/YsBg8ezDoiJz169Aj9+vWrdkWTJHr48CGmTp0qLBlYFX3ui7p+/ToOHDiAEydOwMLCAn/99RcUFRVZxyIcJyUlBXV1deEDyuzsbKipqQlLvwoEAuTk5NB7jdRLRkYG3N3dERISgszMTLHrtiS/jpSVlfHw4UO0bduWdRRCOI2mopFGpXIVwIEDB4T18XNycuDi4oI+ffpgxowZsLe3h5ubGy5evMg4bcN6//49SktLa9xfUlIi8TWFq1qwYAFev34NAFi5ciWGDh2K3377DXJycvD392cbjiNmzZqFLl264Ny5czTDoho0C75uampqCAsLw5UrV4Ql3ywtLTFo0CDW0Thj2bJliIyMxLVr12BtbS3cPmjQIKxcuZIGBaqRlJSEwsJCmJmZifXQkVSjRo3C0aNH4e3tDR6PBw8PD1haWuLMmTM0IADg9OnTIj8LBAK8fv0aO3bsQO/evRml4h4nJye0bt0a+/btg66uLn3u16Jr165ITk5GTEwMHj16hJKSEhoUqOLhw4eIjY0Fj8eDubk5rViqcODAAdYROM/Z2blex+3fv/8/TsJ906ZNQ2pqKlasWEHf1T5jbm6OrKws1jEI4TxaKUAalRYtWuDy5ctiqwCePHmCIUOGIC0tDeHh4RgyZIjEfQiYmZlh+fLlmDx5crX7Dx48CC8vL4lfIl9QUIDFixfj1KlTKCkpwaBBg7Bt2zYoKSkhLi4OLVu2hLa2NuuYnKCsrIzIyMgaV59IOpoFT/4NBgYGOHr0KHr06CGy4iQxMRGWlpYSPYO5pKQEa9euRXh4OHr06IGlS5di8uTJwoG4Nm3aIDg4GIaGhmyDEs77fPCIx+OhadOmGDBgADZt2gQ9PT1GybhFVVUVjx49os/9Wty+fRv79+/HsWPH0Lp1azg5OcHe3h4aGhqso3FCZmYmJk2ahGvXrkFDQwMCgUBYF/7IkSPUT4jUqbLXW6dOnWps6g2U99OTdKqqqrhx4wY6duzIOgonVL1nfvDgAX766Sd4e3vDwsJCrL9J5QRTQiQdrRQgjcqHDx+QmZkpNijw5s0b4YeAhoYGiouLWcRjasyYMVi+fDkGDx4MXV1dkX3p6en46aefahwwkCQrV66Ev78/HBwcoKioiN9//x2zZ8/G8ePHYWlpyToep3Tv3h2JiYn0cKAGNAu+Znfv3sW7d+8wbNgw4bbAwECsXLkS+fn5sLOzw/bt2yEvL88wJTe8efNG2N+kqvz8fImf8bV06VIcPHgQNjY22L9/P+7du4f4+Hj8/vvvkJKSwpo1a7B8+XJqhg7A2NgY9+/fR5MmTUS2Z2dnw9LSEklJSYySccPntfFJ9QYOHEiTAWqwfv16HDhwAG/fvoWDgwPCwsJgYWHBOhbnzJs3Dzk5OXjy5AnMzMwAADExMZg6dSpcXV1x+PBhxgm54cWLF+DxePjqq68AAPfu3cPvv/8Oc3NzzJw5k3E6tmbNmoUjR44gKSkJzs7OmDx5skT2C6wPfX39WgdOJI2GhobIvbNAIMDAgQNFjqFGw4SIopUCpFFxcHDA7du3sWnTJnTt2hU8Hg/37t2Du7s7evXqhYMHD+LIkSPYuHEjHjx4wDpug8rNzUXPnj2RmpqKyZMno02bNuDxeIiNjcVvv/0GfX193LlzB6qqqqyjMmViYgIvLy9MmjQJQPlNeO/evVFUVARpaWnG6bjl5MmT+Omnn7B48eJqZ1hIcsNBUrthw4ahf//+WLJkCQAgOjoalpaWmDZtGszMzLBhwwZ89913WLVqFdugHNCvXz+MGzcO8+bNg6qqKqKiomBkZIS5c+ciMTERFy5cYB2RGQMDA+zcuRPDhw/H06dP0bZtW5w7d0442BQaGgoHBwe8fPmScVL2pKSkkJ6eLjbAlJGRgZYtW+Ljx4+MknFTaWkpioqKqGHlZ7KysjB16lR069YN7du3F/vct7GxYZSMPSkpKbRs2RIjR46EnJxcjcdt3ry5AVNxj7q6Ov766y907dpVZPu9e/cwZMgQZGdnswnGMd988w1mzpwJR0dHpKeno3Xr1mjfvj2ePn0KV1dXeHh4sI7I1MePHxEUFIT9+/fj1q1bGDFiBFxcXDBkyBCJnzBR1aVLl7Bp0yb4+fnRqkmU3xfWV79+/f7DJIQ0HjQoQBqVvLw8uLm5ITAwUFg/X0ZGBlOnToWvry+UlZUREREBABK5jO7Dhw9YtmwZjh49KuwfoKmpiYkTJ8Lb25uWNgOQk5PD8+fP0aJFC+E2RUVFPH36FPr6+gyTcU91tbp5PJ5Ez7DYtm1bvY91dXX9D5Nwm56eHs6cOYMuXboAAJYvX47Q0FCEhYUBAI4fP46VK1ciJiaGZUxOuHXrFqytreHg4AB/f3989913ePLkCW7fvo3Q0FB07tyZdURmZGVlkZycLLxeKyoqIioqCq1atQIAvH79Gvr6+rX20/nSVdbKt7OzQ0BAANTV1YX7+Hw+QkJCcPnyZcTHx7OKyFRwcDDevn0LR0dH4TYvLy+sWbMGpaWlGDBgAI4ePQpNTU2GKbnj9OnTcHR0RG5urtg+Sf3cr9S/f/86H0byeDxcuXKlgRJxU03lTKiptyhNTU3cuXMHbdq0wbZt23D06FHcvHkTly5dwqxZsyR+dVdVKSkp8Pf3R2BgIEpKShATEyPRA7qampoi16L8/HyUlpZCSUlJbCD33bt3DR2PENLI0KAAaZTy8vKQlJQEgUAAExMTib4x+FxKSgqaNm2K/Px8CAQCNG3alGZUVCEtLY309HSRmqZVZ+eST1JSUmrdb2Bg0EBJuKO+rxEejyfRX+gUFBSQkJAgHGjr06cPrK2t8dNPPwEAkpOTYWFhUe2DJ0kUHR2NjRs34uHDh8IyVEuWLJH40hSfz36v2nMBKJ8F37x5c4l+UFk5eFs5YFuVrKwsDA0NsWnTJowcOZJFPOYGDBiAsWPHYs6cOQDKB+G++eYbeHp6CnsxDRs2TOJnd1cyNDTEyJEjsWLFCrFSlITUh62tLbKzs3H48GE0b94cAJCWlgYHBwdoampSHfgKKioqePz4MQwNDWFjY4PevXtjyZIlSE1NRZs2bVBYWMg6ImekpqbC398f/v7+KC4uRlxcnER/9w8ICKj3sVOnTv0Pk3BTQkICPDw84OfnJ9Y34MOHD5g9ezbWrl0rvJckRNLRoAAhX5CysjIoKCjgyZMnwpmURJSUlBSGDRsmUsv8zJkzGDBggEhj2KCgIBbxCPkiGBgY4ODBg+jbty+Ki4uhoaGBM2fOCOt6RkdHo1+/fjSDidRKSkpKZPb7t99+iy1btggfVmZnZ8PJyUmiBwUqGRkZ4f79+9DW1mYdhVN0dHRw8eJFdOrUCQCwcOFCxMTECMtyBQcHY/78+UhISGAZkzNUVVUREREBExMT1lFII/XixQvY2tri8ePH0NfXB4/HQ2pqKiwsLPDnn38Ka+hLuu7du8PKygojRozAkCFDcOfOHXz99de4c+cOxo0bJ/Fl8aqWDwoLC8PIkSPh5OQEa2vralcyE1Jp5syZ0NDQwPr166vdv2TJEuTk5GDnzp0NnIwQbqJGw4R8QaSkpNCqVSu8ffuWBgVqUN2MCWrAXLODBw9i165deP78OW7fvg0DAwNs2bIFRkZGsLW1ZR2PMyrH12lVTjlra2ssXboUP//8M06dOgUlJSV88803wv1RUVES/9CprKwMZWVlkJH5dCuWkZGBXbt2IT8/HzY2NujTpw/DhNzw+TX7u+++E/mZ3nPlnj9/zjoCJ+Xm5oo0Xw4LC8O4ceOEP7dr1w6vXr1iEY2TxowZg6tXr0r89bk2CxcurHY7j8eDgoICTE1NYWtrK7GNUfX19REeHo7Lly8jLi4OAoEA5ubmGDRoEOtonPLzzz9j9OjR2LBhA6ZOnYqvv/4aQHkJr27dujFOx9b333+PI0eOoGXLlnBycsKRI0dEruMEePXqFTZv3gwPD49qZ8OvXbsW7u7uErni6/r16zh48GCN+ydMmAB7e/sGTEQIt9FKAUK+MOfOnYOPjw927tyJ9u3bs45DGrGdO3fCw8MDCxYsgJeXFx4/fgxjY2P4+/sjICAAV69eZR2RuX379sHX11c4y7RVq1ZYsGABpk+fzjgZW2/evMGYMWNw8+ZNqKioICAgAKNHjxbuHzhwIHr06AEvLy+GKdlycnKCrKwsdu/eDaD84WW7du1QVFQEPT09xMTE4M8//8Tw4cMZJyWNRWhoKDZu3IjY2FjweDyYmZlh8eLFIgNyksbExAS//vorhg4diry8PDRp0gRXrlxB7969AQDh4eEYOnQo3rx5wzgpN3h5eWHLli0YMWIELCwsxOpTS3KvnEpWVlYIDw8Hn89HmzZtIBAIkJCQAGlpabRt2xbx8fHg8XgICwuDubk567gN5sqVK5g7dy7u3LlT7UPKXr16YdeuXRJ9Pfocn89HTk6OSE+T5ORkKCkpiTWNlySVTb07depU68C/JK/qdnd3R05OjvAe8nOzZs2Curo6fv755wZOxp6ioiLi4uJqLHObkpICMzMzFBQUNHAyQriJBgUI+cJoamqioKAApaWlkJOTg6Kiosh+KtdB6svc3Bze3t6ws7MTqeX9+PFj9O/fH1lZWawjMrVixQr4+vpi3rx56NmzJwDg9u3b2LFjB+bPn4+1a9cyTsjehw8foKKiAmlpaZHt7969g6qqqtgDJ0nSunVr7NixA0OGDAEA/PLLL/Dy8kJsbCzU1dWxZMkS3Lt3jwbfSL0cOnQITk5OGDNmDHr37g2BQIBbt27h5MmT8Pf3l9hZcUuWLMHp06fx448/Ijg4GLdu3UJSUpLwmrR7924EBgYKm6BLutr65kh6r5xKW7ZswY0bN3DgwAHhw++cnBy4uLigT58+mDFjBuzt7VFYWIiLFy8yTttwbGxsYGVlBTc3t2r3b9u2DVevXqWeAhUKCwshEAigpKQEoPxB5cmTJ2FmZoahQ4cyTsfWtGnT6rUK8MCBAw2Qhpvat2+PXbt21bii9NatW5gxYwaePHnSwMnYa9asGX7//XcMGDCg2v0hISFwcHBAenp6AycjhJtoUICQL0xdzYckseEQ+f9UnWlRdVAgISEBHTp0kPgmaNra2ti+fTu+/fZbke2HDx/GvHnzJH7QBACcnZ2xdetWqKqqimzPz8/HvHnzsH//fkbJ2FNWVsbjx4+FD+HGjBmDFi1aYPv27QCAmJgY9O/fH5mZmSxjckZ1pcx8fX1hbGxMpcwAmJmZYebMmWIP5DZv3ow9e/YgNjaWUTK2CgoK8N133+Hs2bNo1qwZdu/eLTJT2crKCtbW1liyZAnDlKQxadGiBS5fviy2CuDJkycYMmQI0tLSEB4ejiFDhkjUfYCBgQEuXLgAMzOzavfHxcVhyJAhSE1NbeBk3DRkyBCMGTMGs2bNQnZ2Ntq2bQtZWVlkZWVh8+bNmD17NuuIhMOUlZURGxuLli1bVrs/NTUVZmZmyM/Pb+Bk7E2YMAElJSU1DkDa2tpCTk4Ox48fb+BkhHATdWkh5AszderUWv9HSH0ZGRkhIiJCbPv58+clakl8Tfh8Prp06SK2vXPnzigtLWWQiHsCAgKqHTwqLCxEYGAgg0TcoaCgIHJu7ty5gx49eojsz8vLYxGNc3bu3ImFCxdi+PDhyM7OFjYW1tTUxJYtW9iG44ikpCSMGjVKbLuNjY1E9xtQUlLCwYMH8f79e8TGxoqVLrl69SoNCJC/5cOHD9UO1r558wY5OTkAAA0NDRQXFzd0NKYyMjJqXf0nIyNDZbqqCA8PF16P/vjjD+jq6iIlJQWBgYHYtm0b43Tc4OzsjNzcXLHt+fn5cHZ2ZpCIOxQVFZGcnFzj/uTkZLFqAZJi2bJlOH/+PMaNG4d79+7hw4cP+PDhA+7evYuxY8fi4sWLWLZsGeuYhHAGNRom5AvE5/Nx6tQpYV1hc3Nz2NjYiJXwIKQ2ixcvxpw5c1BUVASBQIB79+7h8OHDWLduHfbu3cs6HnOTJ0/Gzp07sXnzZpHtu3fvhoODA6NU3JCTkwOBQACBQIDc3FwoKCgI9/H5fAQHB0t0vVwA+Prrr3Hw4EGsW7cON27cQEZGhshS52fPnqF58+YME3LH9u3bsWfPHtjZ2cHHx0e4vUuXLnB3d2eYjDv09fUREhICU1NTke0hISHQ19dnlIo0Ri9fvsTp06eRmpoq9mD78887SWRrawtnZ2ds2rQJXbt2BY/Hw7179+Du7g47OzsAwL1799C6dWu2QRtYixYtEB0dLXYNqhQVFQU9Pb0GTsVdBQUFwlWUly5dwpgxYyAlJYUePXogJSWFcTpuCAgIgI+Pj9hq08qJJZK82rR79+44ePAg+vbtW+3+wMBAiW1Y3alTJ/zxxx9wdnYWWy3QpEkTHDt2DJaWlozSEcI9NChAyBcmMTERw4cPR1pamrAB2tOnT6Gvr49z587BxMSEdUTSSDg5OaG0tBQ//PADCgoKYG9vjxYtWmDr1q2YNGkS63icsG/fPly6dEk4w/vOnTt48eIFpkyZgoULFwqPk7QHKRoaGuDxeODxeNU+GOHxeFi9ejWDZNyxYsUKDB8+HMeOHcPr168xbdo0kQcmJ0+eFDZDlXTPnz9Hp06dxLbLy8tL5NL46ixatAiurq6IiIhAr169hI1O/f39sXXrVtbxmMvIyIC7uztCQkKQmZmJz6unVq4+kXQhISGwsbGBkZER4uPj0b59eyQnJ0MgENBDlAp+fn5wc3PDpEmThKsCZWRkMHXqVPj6+gIA2rZtK3GTJ4YPHw4PDw8MGzZMZCIAUP4Qd+XKlRg5ciSjdNxjamqKU6dOYfTo0bh48aKw9FtmZqZYo2ZJQxNL6ubu7o7BgwdDXV0dixcvhq6uLoDyz7r169fD398fly5dYpySnZEjRyIlJQUXLlxAYmIiBAIBWrdujSFDhgj7eBBCylFPAUK+MMOHD4dAIMBvv/0GLS0tAMDbt28xefJkSElJ4dy5c4wTksYoKysLZWVlEn8TXpWVlVW9juPxeLhy5cp/nIZbQkNDIRAIMGDAAJw4cUJ4LQIAOTk5GBgY0Cx4lPcNuHz5Mpo1a4bx48dDSupTVcfdu3ejW7du6NixI7uAHGFubo5169bB1tZWpL/Jtm3bEBAQgIcPH7KOyAknT57Epk2bhP0DzMzMsHjxYuq5AGDYsGFITU3F3LlzoaenJ9bEks5RuW7dusHa2hqenp7C95qOjg4cHBxgbW1Ndc6ryMvLQ1JSEgQCAUxMTKCiosI6ElMZGRmwtLSEtLQ05s6dizZt2oDH4yE2Nha//PIL+Hw+wsPDhQ8vJd0ff/wBe3t78Pl8DBgwAJcvXwYArFu3DtevX8f58+cZJ2RHSkqq1kbDlRNLli9f3oCpuMfPzw/z589HSUkJ1NTUwOPx8OHDB8jKysLX15eu14SQeqFBAUK+MMrKyrhz5w4sLCxEtkdGRqJ3795Uo5r8bZmZmYiPjwePx0ObNm3QtGlT1pFII5GSkoKWLVvW+uWOkLocOHAAK1aswKZNm+Di4oK9e/fi2bNnwlJmkr5yqbS0FF5eXnB2dqZSQTVQVVXFjRs3aJCtDqqqqoiIiICJiQk0NTURFhaGdu3aITIyEra2trXWsCYkJSUFs2fPxsWLF4WrcXg8HoYOHYpff/0VhoaGbANyTHp6Ol6/fo2vv/5aOCng3r17UFNTQ9u2bRmnY4cmltTfy5cvcfz4cZHZ8OPGjcNXX33FOhoTf6cfh6ur63+YhJDGg8oHEfKFkZeXr7YpU15eHuTk5BgkIo1VTk4O5syZg8OHD6OsrAwAIC0tjYkTJ+KXX36Buro644SE6wwMDHDjxg34+fkhKSkJx48fR4sWLXDw4EEYGRmhT58+rCNywsGDB7Fr1y48f/4ct2/fhoGBAbZs2QIjIyOawQwqZVYXGRkZbNiwAVOnTmUdhbP09fXFSgYRccrKyvj48SMAoHnz5nj27BnatWsHoHzFIClvcurj4yMsRVV5f1QpKSmJUTL2DAwMEBwcjPfv3wsfUrZq1Qqampqso3FSs2bNkJeXh8uXL6Nv375QVFQU9qmQZP369QNQXjpQX19fZBUlEaWpqSksPUUgLOFWFx6PR4MChFSgQQFCvjAjR47EzJkzsW/fPmGDobt372LWrFmwsbFhnI40JtOnT0dERATOnTuHnj17gsfj4datW5g/fz5mzJiBY8eOsY7Y4MaMGQN/f3+oqalhzJgxtR4bFBTUQKm468SJE3B0dISDgwPCw8OFD5tyc3Ph7e2N4OBgxgnZ27lzJzw8PLBgwQJ4eXkJa5traGhgy5YtNChQYcaMGZgxYwaVMqvBoEGDcO3aNUybNo11FE7asmULli5dCj8/P5qtXIsePXrg5s2bMDc3x4gRI7Bo0SJER0cjKChI2DtH0k2fPh2hoaFwdHSsthQVKX9Q2bVrV9YxOO3t27eYMGECrl69Ch6Ph4SEBBgbG2P69OnQ0NDApk2bWEdkzsDAANnZ2bh37161A3BTpkxhlIw7dHV1MWHCBDg7O9NEG5QPJBFC/h4qH0TIFyY7OxtTp07FmTNnICsrC6C8tICNjQ38/f1pdjepN2VlZVy8eFHsJvPGjRuwtraWyAafTk5O2LZtG1RVVTFt2rRaHwYcOHCgAZNxU6dOneDm5oYpU6aI1IKPiIiAtbU10tPTWUdkztzcHN7e3rCzsxM5R48fP0b//v1pdi6pFz8/P6xatQoODg7o3LkzlJWVRfZL4qQATU1NkWt0fn4+SktLoaSkJLw/qvTu3buGjsdJSUlJyMvLQ4cOHVBQUAB3d3eEhYXB1NQUvr6+MDAwYB2ROQ0NDZw7d44awZN/ZMqUKcjMzMTevXthZmYm/Oy/dOkS3Nzc8OTJE9YRmTtz5gwcHByQn58PVVVVkes5j8ej6zbKz5G/vz/Onj0LAwMDODs7Y8qUKVReiRBSbzQoQMgXKiEhAXFxcRAIBDA3N4epqSnrSKSRadmyJc6dOyfWnyIqKgrDhw/Hy5cvGSUjjYWSkhJiYmJgaGgo8sA7KSkJ5ubmKCoqYh2ROUVFRcTFxcHAwEDkHCUkJKBDhw4oLCxkHZG5jIwMuLu7C8t1fH7rWrm6QpLVVl6Bx+NJ5DkKCAio97FUeonUl5GREYKDg2FmZsY6CmnEmjVrhosXL+Lrr78W+ex//vw5LCwsqAccgNatW2P48OHw9vaGkpIS6zic9vbtWwQGBsLf3x8xMTEYOnQonJ2dYWNjAxkZyS0O8vLlS5w+fRqpqakoLi4W2bd582ZGqQjhFsm9QhDyhWvVqhVatWrFOgZpxH766ScsXLgQgYGB0NPTA1DeFG3x4sVYsWIF43TsDRgwAEFBQdDQ0BDZnpOTAzs7O1y5coVNMA7R09NDYmKiWLmOsLAwGBsbswnFMUZGRoiIiBCbgXv+/HmYm5szSsUt06ZNQ2pqKlasWEHlOmrweVkFQg/6/1/Z2dn4448/8OzZMyxevBhaWloIDw+Hrq4uWrRowToec2vWrIGHhwcCAgLoQSX5v+Xn51f7+snKyoK8vDyDRNyTlpYGV1dXep/VQ5MmTeDm5gY3Nzds374dixcvRnBwMLS1tTFr1iwsXbpU4s5jSEgIbGxsYGRkhPj4eLRv3x7JyckQCASwtLRkHY8QzqBBAUK+AAsXLsSaNWugrKyMhQsX1nosjYqT+tq5cycSExNhYGCAli1bAgBSU1MhLy+PN2/ewM/PT3hseHg4q5jMXLt2TWzWCQAUFRXhxo0bDBJxz3fffYf58+dj//794PF4ePXqFW7fvg13d3d4eHiwjscJixcvxpw5c1BUVASBQIB79+7h8OHDWLduHfbu3cs6HieEhYXhxo0b6NixI+sonJSSkoJLly6htLQU/fr1o8GkagQHB0NaWhpDhw4V2X7p0iXw+XwMGzaMUTJuiYqKwqBBg6Curo7k5GTMmDEDWlpaOHnyJFJSUhAYGMg6InObNm3Cs2fPoKurC0NDQ7FSVJJ4P0T+vr59+yIwMBBr1qwBUL6iq6ysDBs2bICVlRXjdNwwdOhQPHjwgCaR1EN6ejoCAwNx4MABpKamYty4cXBxccGrV6/g4+ODO3fu4NKlS6xjNqhly5Zh0aJF8PT0hKqqKk6cOAEdHR04ODjA2tqadTxCOIMGBQj5Ajx69AglJSXCP9eEZleSv8POzo51BE6KiooS/jkmJkakLj6fz8eFCxdoNmWFH374AR8+fICVlRWKiorQt29fyMvLw93dHXPnzmUdjxOcnJxQWlqKH374AQUFBbC3t0eLFi2wdetWTJo0iXU8TtDX1xcrGUTKXb9+HcOHD0dBQQEAQEZGBgEBAfj2228ZJ+OWpUuXwsfHR2x7WVkZli5dSoMCFRYuXIhp06Zh/fr1UFVVFW4fNmwY7O3tGSbjDro3Iv+GDRs2oH///njw4AGKi4vxww8/4MmTJ3j37h1u3rzJOh4njBgxAosXL0ZMTAwsLCzEBuAksVfO54KCgnDgwAFcvHgR5ubmmDNnDiZPniyyirljx47o1KkTu5CMxMbG4vDhwwDK740KCwuhoqICT09P2NraYvbs2YwTEsIN1FOAEEII+RukpKSEA2zVfYQqKipi+/btcHZ2buhonFVQUICYmBiUlZXB3NwcKioqrCNxUlZWFsrKyqCjo8M6CqdcunQJmzZtgp+fn1gpKknXr18/qKmpwc/PD4qKili2bBnOnTuHFy9esI7GKYqKioiNjRV7/SQnJ6Ndu3bIz89nE4xj1NXVER4eDhMTE5E65ykpKWjTpg31gSHkX5Seno6dO3fi4cOHKCsrg6WlJebMmSMs2SnpqFdO3dTV1TFp0iRMnz4dXbt2rfaYwsJCrF+/HitXrmzgdGw1a9YMV65cgbm5Odq1a4d169bBxsYGkZGR6N27N/XtIKQCrRQghBBSI6otLO758+cQCAQwNjbGvXv30LRpU+E+OTk56OjoQFpammFC7lFSUkKXLl1Yx+A8bW1t1hE4aeLEiSgoKICJiQmUlJTEZgu+e/eOUTL2oqOjcf36dTRv3hxAeWmTPXv24P3799DU1GScjjvU1dWRlJQkNiiQmJgIZWVlNqE4SEFBATk5OWLb4+PjRT7rCCH/XLNmzbB69WrWMTiLeuXU7fXr13X2ClBUVJS4AQEA6NGjB27evAlzc3OMGDECixYtQnR0NIKCgtCjRw/W8QjhDBoUIOQLk5+fDx8fH4SEhCAzM1PshiopKYlRMtLYUG3h6lU2hKUvK3Wj61HdMjIy4O7uLjxHn68+oZlwwJYtW1hH4Kzs7GyRlSXKyspQUlJCdnY2DQpUYWNjgwULFuDkyZMwMTEBUD4gsGjRIipBUYWtrS08PT1x7NgxAOWzcVNTU7F06VKMHTuWcTp2tLS08PTpU2hra0NTU7PWcpySPEhJale1/GRdOnTo8B8maXyKioqgoKDAOgYnfD5wW91AbiU1NbX/Og5nbd68WbgaYNWqVcjLy8PRo0dhamoKX19fxukI4Q4qH0TIF+bbb79FaGgoHB0doaenJ/bFZf78+YySkcZm0KBBsLS0FNYWriwjcOvWLdjb2yM5OZl1xAZ3+vTpeh9LD5roelQfw4YNQ2pqKubOnVvtObK1tWWUjDQGUlJSuHLlCrS0tITbevXqhWPHjuGrr74SbpP0B0wfPnyAtbU1Hjx4IDwvL1++xDfffIOgoCCR+suSLCcnB8OHD8eTJ0+Qm5uL5s2bIz09HT179kRwcLDErqoICAjApEmTIC8vj4CAgFqPnTp1agOlIo1NZfnJuh6/UGmccnw+H97e3ti1axcyMjLw9OlTGBsbY8WKFTA0NISLiwvriExULWNaE4FAQK8jQki90KAAIV8YDQ0NnDt3Dr1792YdhTRyVFtYXG31TauiG/FydD2qm6qqKm7cuIGOHTuyjsIpOTk5whlutc2CAyR7JlxtD5kqt9P1qJxAIMDly5cRGRkJRUVFdOjQAX379mUdi5OuXLmC8PBwYZ3zQYMGsY5ESKOXkpJS72MrV6VKMk9PTwQEBMDT0xMzZszA48ePYWxsjGPHjsHX1xe3b99mHZGJ0NDQeh/br1+//zAJtxkbG+P+/fto0qSJyPbs7GxYWlrSamVCKlD5IEK+MJqamiIzBgn5f1FtYXFUMujvoetR3fT19eucNSiJNDU18fr1a+jo6EBDQ6PaWXH0wLu8xwmpHx6PhyFDhqBv376Ql5evc6alpCktLYWCggIiIiIwYMAADBgwgHUkTsvMzKy2LJ6kr8ohNat80F9SUoKZM2dixYoVMDY2ZpyKuwIDA7F7924MHDgQs2bNEm7v0KED4uLiGCZjS5If9P8dycnJ1d4ffvz4EWlpaQwSEcJNNChAyBdmzZo18PDwQEBAQJ2NhwipDdUWJv8UXY/qtmXLFixduhR+fn5iTVAlWdWSOFevXmWchrtoNmn9lJWVwcvLi8pQ1EJGRgYGBgYSPchWHw8fPsTUqVMRGxsrNqAr6YOUpH5kZWVx8uRJrFixgnUUTktLS4OpqanY9rKyMpSUlDBIxE3Z2dm4d+9etYOUU6ZMYZSKnaqlXi9evAh1dXXhz3w+HyEhIXS/TUgVVD6IkC9Mp06d8OzZMwgEAhgaGkJWVlZkf3h4OKNkpLGh2sK18/T0rHW/h4dHAyXhLroe1U1TUxMFBQUoLS2FkpKS2DmippWkvm7cuAE/Pz88e/YMf/zxB1q0aIGDBw/CyMgIffr0YR2PKSpDUT8HDhzA8ePHcejQIVrlVYMOHTrA1NQUS5Ysga6urtiKExqoI/Xh5OQECwsLLFy4kHUUzurSpQsWLFiAyZMni5QxXb16Nf766y/cuHGDdUTmzpw5AwcHB+Tn50NVVVXkesTj8STyHrKy1Gt1pRVlZWVhaGiITZs2YeTIkSziEcI5tFKAkC+MnZ0d6wjkC6GmpoawsDBcvXoVDx8+pNrCnzl58qTIzyUlJXj+/DlkZGRgYmJCgwKg61F9bNmyhXUEToqKiqr3sVSuAzhx4gQcHR3h4OCAR48e4ePHjwCA3NxceHt7Izg4mHFCtqgMRf1s27YNiYmJaN68OQwMDMQG/2kgt7xkV1BQULUzmAmpL1NTU6xZswa3bt1C586dxd5rrq6ujJJxx8qVK+Ho6Ii0tDSUlZUhKCgI8fHxCAwMxNmzZ1nH44RFixbB2dkZ3t7etCK3QuVqCSMjI9y/fx/a2tqMExHCbbRSgBBCiJiysjL4+/sjKCgIycnJ4PF4MDIywrhx4+Do6Ei1mGuQk5ODadOmYfTo0XB0dGQdh6nS0lJ4eXnB2dkZ+vr6rOOQRqa2BrpVUbmOcp06dYKbmxumTJkiMqMyIiIC1tbWSE9PZx2RKUVFRcTFxcHAwEDk/MTExKBbt27Iy8tjHZETVq1aVevn+8qVKxswDTfZ2dnB0dGRyiiSf8TIyKjGfTwej5qgVrh48SK8vb1FJid5eHhgyJAhrKNxgrKyMqKjo6k3BSHk/0aDAoR8gbKzs/HHH3/g2bNnWLx4MbS0tBAeHg5dXV20aNGCdTzCcQKBAKNGjUJwcDC+/vprtG3bFgKBALGxsYiOjoaNjQ1OnTrFOiZnPX78GCNHjkRycjLrKMypqqoiOjqaand+JicnB2pqasI/16byOEmTkpJS72OpXAegpKSEmJgYGBoaijz0TkpKgrm5OYqKilhHZIrKUJB/S1ZWFqZOnYpu3bqhffv2YiXfbGxsGCUjhEiaMWPGYNKkSZgwYQLrKJwUGhqKjRs3IjY2FjweD2ZmZli8eDG++eYb1tEI4QwqH0TIFyYqKgqDBg2Curo6kpOTMWPGDGhpaeHkyZNISUlBYGAg64iE4/z9/XH9+nWEhITAyspKZN+VK1dgZ2eHwMBAiWxeVR/Z2dn48OED6xicMHDgQFy7dg3Tpk1jHYVTNDU18fr1a+jo6EBDQ6PambkCgUCiZ8HTg/6/R09PD4mJiWIDcGFhYRI9g9DZ2Rlbt26lMhT1ZGxsjPv376NJkyYi27Ozs2FpaUmzlwHcunULYWFhOH/+vNg+Sb5mk/9PcXExnj9/DhMTE8jI0KOZmuTl5Yk10ZXUSRNVG+mOGDECixcvRkxMDCwsLGiQsopDhw7ByckJY8aMgaurKwQCAW7duoWBAwfC398f9vb2rCMSwgm0UoCQL8ygQYNgaWmJ9evXi8yGu3XrFuzt7Wn2MqnTkCFDMGDAACxdurTa/d7e3ggNDcXFixcbOBm3bNu2TeRngUCA169f4+DBg+jbty8OHz7MKBl3+Pn5YdWqVXBwcKi2Zq6kflkJDQ1F7969ISMjg9DQ0FqP7devXwOl4ranT5/i2rVryMzMFHswQP07gPXr1yMgIAD79+/H4MGDERwcjJSUFLi5ucHDwwNz585lHZEJaWlp4QAclaGom5SUFNLT06GjoyOyPSMjA/r6+iguLmaUjDsMDQ0xcuRIrFixArq6uqzjkEaqoKAA8+bNQ0BAAIDyzzhjY2O4urqiefPmNd6DS5Lnz59j7ty5uHbtmshqN0mfNFHZSLcuknyOAMDMzAwzZ86Em5ubyPbNmzdjz549iI2NZZSMEG6hQQFCvjDq6uoIDw+HiYmJyKBASkoK2rRpI/ElBEjdmjVrhgsXLqBjx47V7n/06BGGDRsm8TWqP68HKyUlhaZNm2LAgAFYtmwZVFVVGSXjjtq+uEj6lxVSf3v27MHs2bOhra2NZs2aiays4PF41Py0wvLly+Hr6yv8nJeXl4e7uzvWrFnDOBk7NT3kJqIqZ57a2dkhICAA6urqwn18Ph8hISG4fPky4uPjWUXkDFVVVURERMDExIR1FNKIzZ8/Hzdv3sSWLVtgbW2NqKgoGBsb4/Tp01i5ciUePXrEOiJzvXr1AlB+rnR1dcVWVdKkCVIbeXl5PHnyRKwpfGJiItq3b0/PRAipQGvUCPnCKCgoVFujOj4+Hk2bNmWQiDQ27969q3X2m66uLt6/f9+Aibjp+fPnrCNw3uczukm5qKioeh/boUOH/zBJ47B27Vp4eXlhyZIlrKNwmpeXF5YvX46YmBiUlZXB3NwcKioqrGMxV1vjXFLOzs4OQPm5mjp1qsg+WVlZGBoaYtOmTQyScc+YMWNw9epVGhQg/8ipU6dw9OhR9OjRQ+QaZW5ujmfPnjFMxh1RUVF4+PAh2rRpwzoKZwUGBmLixImQl5cX2V5cXIwjR45IdKlXfX19hISEiA0KhISEQF9fn1EqQriHBgUI+cLY2trC09MTx44dA1D+BS81NRVLly7F2LFjGacjjQGfz6+1rqm0tDRKS0sbMBG3ODs71+u4/fv3/8dJGpeioiIoKCiwjsEJHTt2BI/HQ12LNWk1Rbn3799j/PjxrGNw2ocPH8Dn86GlpYUuXboIt7979w4yMjISW3sZAFq3bl3nwMC7d+8aKA03VQ7gGhkZ4f79+9DW1maciLtat26NZcuWISwsrNoa3q6uroySkcbkzZs31a5gys/Pp4HMCl27dsWLFy9oUKAWTk5OsLa2Fnst5ebmwsnJSSIHBSp7CS1atAiurq6IiIhAr169wOPxEBYWBn9/f2zdupV1TEI4g8oHEfKFycnJwfDhw/HkyRPk5uaiefPmeP36NXr27Inz58+L1fQm5HNSUlIYNmyY2KyTSh8/fsSFCxck9mGllJQUDAwM0KlTp1of6p48ebIBU3ETn8+Ht7c3du3ahYyMDGHN3BUrVsDQ0BAuLi6sIzKRkpJS72Op4S7g4uKCrl27YtasWayjcNawYcMwatQofP/99yLbd+3ahdOnTyM4OJhRMrakpKSwZcsWkXI41fl8drykuXv3Lt69e4dhw4YJtwUGBmLlypXIz8+HnZ0dtm/fXuN9gST5vHRgVTwej5oxk3rp168fxo0bh3nz5kFVVRVRUVEwMjLC3LlzkZiYiAsXLrCOyNyzZ88wa9YsTJ48Ge3btxcbgKOVlOWfcRkZGWLVACIjI2FlZSWRA95VewmdPHkSmzZtEvYPMDMzw+LFi2Fra8s4JSHcQSsFCPnCqKmpISwsDFeuXEF4eDjKysrQuXNnDBw4kHU00kjU5+GIJM48qTRr1iwcOXIESUlJcHZ2xuTJk6GlpcU6Fid5eXkhICAA69evx4wZM4TbLSws4OvrK7GDAvSg/+8xNTXFihUrcPv2bXTo0IFm5lbj7t272Lx5s9j2/v37Y/ny5QwSccekSZOop0AdVq5cCSsrK+GgQHR0NFxcXDBt2jSYmZlhw4YNaN68OVatWsU2KAdQ6UDyb1i3bh2sra0RExOD0tJSbN26FU+ePMHt27cRGhrKOh4nvHnzBs+ePYOTk5NwW+UqS0lfSdmpUyfweDzweDwMHDhQZIU3n8/H8+fPYW1tzTAhO1UnbI0ePRqjR49mmIYQ7qOVAoR8Iaqb5RUQEICVK1eioKCAZnkR8i/6+PEjgoKCsH//fty6dQsjRoyAi4sLhgwZQsu+qzA1NYWfnx8GDhwo0vg8Li4OPXv2pN4UFZ4+fYpr164hMzNTrA+Dh4cHo1TcQTNz66asrIw7d+7AwsJCZHt0dDS6d++OgoICRsnYqjpjkNRMT08PZ86cEZaeWr58OUJDQxEWFgYAOH78OFauXImYmBiWMQn5okRHR2Pjxo14+PAhysrKYGlpiSVLlohdxyWVubk5zMzM8MMPP1TbaFiSJ1isXr1a+H8XLVok0j9ITk4OhoaGGDt2LOTk5FhFZKam1ROEkOrRoAAhX4hhw4ahf//+wkaM0dHR6Ny5M6ZOnSqc5fXdd9/RLC9C/mUpKSnw9/dHYGAgSkpKEBMTQ809KygqKiIuLg4GBgYigwIxMTHo1q0b8vLyWEdkbs+ePZg9eza0tbXRrFkzkS+9PB4P4eHhDNNxS1ZWFng8Hpo0acI6Cuf0798fFhYW2L59u8j2OXPmICoqCjdu3GCUjC0pKSmkp6fToEAdFBQUkJCQIGy+2KdPH1hbW+Onn34CACQnJ8PCwgK5ubksY3LGy5cvcfr0aaSmpqK4uFhkX3Urdgghf5+ysjIiIyPFGsWSTwICAjBx4kTq2VWFlJQU1NXVqZcQIfVE5YMI+UJERERgzZo1wp+PHDmCbt26Yc+ePQAAfX19rFy5kgYFCPmXVS7fFQgEYrO8JV27du1w48YNsdlcx48fR6dOnRil4pa1a9fCy8tLOKBLRGVnZ2P58uU4evSocGWJpqYmJk2aBC8vrzprxUsKLy8vDBo0CJGRkcJygSEhIbh//z4uXbrEOB07dE2uH11dXTx//hz6+vooLi5GeHi4cCYqUN608vOyXZIqJCQENjY2MDIyQnx8PNq3b4/k5GQIBAJYWlqyjkcaET6fj5MnTyI2NhY8Hg9mZmawtbUVKQUjyQYMGECDAnWoLPlaXFxc7WrTli1bsojF3OrVq+n+kJB6ok8cQr4Q79+/h66urvDn0NBQkVqCXbt2xYsXL1hEI+SLU7V8UFhYGEaOHIkdO3bA2toaUlJSrONxxsqVK+Ho6Ii0tDSUlZUhKCgI8fHxCAwMxNmzZ1nH44T3799j/PjxrGNw0rt379CzZ0+kpaXBwcEBZmZmEAgEiI2Nhb+/P0JCQnDr1i1oamqyjspc7969cfv2bWzYsAHHjh2DoqIiOnTogH379qFVq1as4xGOs7a2xtKlS/Hzzz/j1KlTUFJSwjfffCPcHxUVBRMTE4YJuWPZsmVYtGgRPD09oaqqihMnTkBHRwcODg4SW8Ob/H2PHz+Gra0t0tPT0aZNGwDlpQSbNm2K06dPUwkhAKNGjYKbmxuio6NhYWEhNjBpY2PDKBl3JCQkwNnZGbdu3RLZLul9F6iXECH1R+WDCPlCGBgY4ODBg+jbty+Ki4uhoaGBM2fOCGcMRkdHo1+/frRUjpB/6Pvvv8eRI0fQsmVLODk5YfLkyVTOpBYXL16Et7e3SM1cDw8PDBkyhHU0TnBxcUHXrl0xa9Ys1lE4Z8GCBQgJCcFff/0lMugNAOnp6RgyZAgGDhwIX19fRgkJ+TK8efMGY8aMwc2bN6GiooKAgACR5owDBw5Ejx494OXlxTAlN6iqqiIiIgImJibQ1NREWFgY2rVrh8jISNja2iI5OZl1RNII9OjRAzo6OggICBAObL9//x7Tpk1DZmYmbt++zTghe7VNspHkB95V9e7dGzIyMli6dCn09PTESuZ8/fXXjJKxQ72ECPl7aFCAkC/Ed999h+joaOEsr4CAALx69UrYYOi3337Dli1bcP/+fcZJCWncpKSk0LJlS3Tq1KnWepVBQUENmIo0VuvWrcPmzZsxfPhwdOjQQWwmnKurK6Nk7BkaGsLPzw9Dhw6tdv+FCxcwa9YsiX0Il5OTAzU1NeGfa1N5HCG1+fDhA1RUVCAtLS2y/d27d1BRUZHIppWfa9asGa5cuQJzc3O0a9cO69atg42NDSIjI9G7d2/qlUPqRVFREQ8ePEC7du1Etj9+/Bhdu3ZFYWEho2SkMVFWVsbDhw/Rtm1b1lE4g3oJEfL3UPkgQr4Qa9euxZgxY9CvXz/hLK+qX972799PM3MJ+RdMmTKlzuZVpJyxsTHu378vtpIiOzsblpaWSEpKYpSMO3bv3g0VFRVcv34d169fF9nH4/EkelDg9evXYg9Mqmrfvj3S09MbMBG3aGpqCmfDaWhoVHtdkvQSAuTvqakGs5aWVgMn4a4ePXrg5s2bMDc3x4gRI7Bo0SJER0cjKCgIPXr0YB2PNBJt2rRBRkaG2GdcZmYm1dCv8Pz5cxgZGbGOwWnm5ubIyspiHYNTqJcQIX8PrRQg5AtDs7wIIVxR02ydjIwMtGzZEh8/fmSUjHuysrLA4/GoFFUVLVq0wNGjR9GnT59q99+4cQOTJk1CWlpaAyfjhtDQUGHpgGvXrtU6WNmvX78GTEbIlyspKQl5eXno0KEDCgoK4O7ujrCwMJiamsLX1xcGBgasI5JGIDg4GD/88ANWrVolHEy6c+cOPD094ePjI/K5J6krvaSlpdG3b1+4uLhg3LhxUFBQYB2Jc65cuYKffvoJ3t7e1fZdkNTXDiGk/mhQgBBCCCH/qtOnTwMA7OzsEBAQIDL7lM/nIyQkBJcvX0Z8fDyriJyQnZ2N5cuX4+jRo3j//j2A8tnfkyZNgpeXV42zdiWFi4sLEhMTcfnyZbEB7Y8fP2Lo0KEwMTHBvn37GCUkhBBC/r6q9fIrB3QrH8tU/VmSV3o9fvwY+/fvx2+//YaPHz9i4sSJcHFxQbdu3VhH44zK19HnkwIk/bVDCKk/GhQghBBCyL+q6peUz28zZGVlYWhoiE2bNmHkyJEs4nHCu3fv0LNnT6SlpcHBwQFmZmYQCASIjY3F77//Dn19fdy6dUvYgFASvXz5El26dIG8vDzmzJkjrJkbExODX3/9FR8/fsSDBw+gr6/POCl7xsbGcHBwwOTJk9GmTRvWcQj5omVnZ+OPP/7As2fPsHjxYmhpaSE8PBy6urpo0aIF63ikEQgNDa33sZK+0qu0tBRnzpyBv78/zp8/j1atWsHFxQWOjo5o2rQp63hM1fU6kvTXDiGkbjQoQAgh5H/t3XtM1fUfx/HXAcEbl4Ym4gW5pXTzQmSp00JQEM3ItWGKiuDMyx9SG0vbzGy5Jak5Y6Y5LlEgaZHaZmahq5GZIuZxqeSFgzqjWOJlYiBwfn/0i/34oYmlfM45PB9/yffDH8/5B07e5/t5A/dEcHCwDh48qJ49e5pOcThpaWkqLi7W119/LX9//xZnVVVVGj9+vKKjo/XOO+8YKnQMFRUVWrBggXbv3t3iU5Tjxo1TZmYmdy//15o1a7R582YdOnRIw4YN04wZM5SYmKiAgADTaYBLsVqtiomJka+vr2w2m8rLyxUSEqKlS5eqsrJSeXl5phMBl1RXV6f169dryZIlqq+vl4eHhxITE7Vy5Ur+rQOAf4ihAAAAQDsLCgrSxo0bFRsbe9PzXbt2ad68ebLZbO0b5qBqamp08uRJSVJYWBiLT2/h559/Vn5+vgoLC3XmzBlFRUUpKSlJM2fONJ0GuISYmBhFREQoIyND3t7eOnLkiEJCQrRv3z5NmzaNn9m4I7W1tTp79qzq6+tbPB88eLChIsdTWlqq7OxsFRYWqnv37po1a5ZSU1N14cIFvfbaa7p69aoOHDhgOtOYS5cuKSsrS8ePH5fFYtFDDz2klJSUDn8FJYC2YSgAAADumeLiYhUXF+u3335TU1NTi7Ps7GxDVeZ17txZp0+fVr9+/W56fv78eYWFhemPP/5o5zK4iv3792v+/PmyWq3cKwzcJb6+viorK1NoaGiLoUBlZaUGDRrEz2y0SXV1tWbPnq0vvvjipuf8zP7zDbicnByVl5crPj5ec+bMUXx8fIt9DKdOnVJ4eLgaGhoMlppTWlqq2NhYde3aVcOHD5fdbldpaamuX7+u3bt3KyIiwnQiAAfXyXQAAABwTcuXL9cbb7yhyMhIBQQEtFqE1pH17NlTNpvtlkOBiooK9ejRo52r4AoOHDiggoICffzxx7p8+bKef/5500mAy+jSpYuuXLnS6nl5eXmHv98cbZeWlqaamhrt379fUVFR+uyzz/Trr7/qzTff1OrVq03nOYT33ntPKSkpmj17tnr37n3T7wkMDFRWVlY7lzmOl156SZMnT9amTZvUqdOfv9praGjQnDlzlJaWpm+//dZwIQBHx5sCAADgnggICFBGRoZmzJhhOsXhpKam6tSpU/rqq6/k6enZ4qyurk6xsbEKDQ3t0P/ZRdv9dW1QQUGBbDaboqKiNH36dE2ZMkXe3t6m8wCXMXfuXFVXV2vLli3y8/OT1WqVu7u7EhISNGbMGK1du9Z0IpxAQECAtm/fruHDh8vHx0elpaUaOHCgduzYoYyMDJWUlJhOhBPo2rWrDh8+rPDw8BbPjx07psjISNXW1hoqA+AseFMAAADcE/X19Ro5cqTpDIe0fPlyRUZG6oEHHtDChQub/0N37NgxrV+/XnV1dfrwww8NV8JZhIeHKzIyUgsXLtTUqVNv+alKAP/OqlWrFB8fr169eun69et66qmnVFVVpREjRmjFihWm8+Akrl27pl69ekmS/Pz8VF1drYEDB+rRRx9VWVmZ4TrHwt6FW/Px8dHZs2dbDQXOnTvHBwIAtAlDAQAAcE/MmTNHBQUFWrp0qekUh9OvXz99//33WrBggZYsWaK/Xty0WCwaN26cMjMz1b9/f8OVcBYnTpzQwIEDTWcALs/Hx0clJSXas2ePysrK1NTUpIiICMXExJhOgxMZNGiQysvLFRQUpKFDh2rjxo0KCgrShg0bFBAQYDrPIVRXVys5OVm7du266Tl7F6TExESlpqZq1apVGjlypCwWi0pKSpSenq4XXnjBdB4AJ8D1QQAA4J5YtGiR8vLyNHjwYA0ePFgeHh4tztesWWOozLHU1NTo5MmTkqSwsDD5+fkZLoIzunTpkj755BOdPn1a6enp8vPzU1lZmfz9/dW3b1/TeYDTa2hoUJcuXfTjjz/qkUceMZ0DJ5afn68bN24oOTlZhw8fVmxsrH7//Xd5enoqNzdXiYmJphONmz59umw2m9auXXvTvQsTJ040nWhcfX290tPTtWHDBjU0NMhut8vT01Pz58/XW2+9pc6dO5tOBODgGAoAAIB7Iioq6m/P9+7d204lgGuzWq2Kjo7WfffdJ5vNpvLycoWEhGjp0qWqrKxUXl6e6UTAJYSGhqqoqEhDhgwxnQInVFtbq/T0dG3btk03btxQTEyM1q1bp27duunEiRMKDAxUz549TWc6BPYutF1tba1Onz4tu92usLAwdevWzXQSACfBUAAAAABwYtHR0XrssceUkZEhb29vHTlyRCEhIdq3b5+mTZsmm81mOhFwCTk5Odq6das++ugj3urCHUtPT9f69es1ffp0de3aVQUFBXr66ae1detW02kOx8fHR1arVUFBQQoKClJ+fr5GjRqliooKPfzwwx16iW5KSkqbvi87O/selwBwduwUAAAAd9WUKVNu+z0Wi0WffvppO9QArq+0tFTvv/9+q+d9+/ZVVVWVgSLANa1bt06nTp1Snz59NGDAAHXv3r3FOUti8XeKioqUlZWlqVOnSvrzipxRo0apsbFR7u7uhuscC3sXbi03N1cDBgzQsGHDxGd8AfwbDAUAAMBd5evrazoB6FC6dOmiK1eutHpeXl6u+++/30AR4JqeffZZWSwW0xlwUufOndPo0aObvx4+fLg6deqkCxcuqH///gbLHE9aWpp++eUXSdKyZcsUGxur/Pz85r0LHdm8efNUWFioM2fOKCUlRUlJSby5BOAf4fogAAAAwInNnTtX1dXV2rJli/z8/GS1WuXu7q6EhASNGTNGa9euNZ0IAB2eu7u7qqqqWgxrvb29ZbVaFRwcbLDMcbB3oW3q6upUVFSk7Oxs7du3TxMnTlRqaqrGjx/P4BJAmzEUAAAAAJzYlStXFB8fr59++klXr15Vnz59VFVVpREjRmjnzp2trjgB8M+EhITo4MGD6tGjR4vnly5dUkREhM6cOWOoDM7Azc1NEyZMUOfOnZufff755xo7dmyLn9NFRUUm8hwCexfuXGVlpXJzc5WXl6cbN27o2LFj8vLyMp0FwAlwfRAAAADgxHx8fFRSUqK9e/fq0KFDampqUkREhGJiYkynAS7FZrOpsbGx1fO6ujqdP3/eQBGcyaxZs1o9S0pKMlDiuNi7cOcsFossFovsdruamppM5wBwIgwFAAAAACfV1NSk3NxcFRUVyWazyWKxKDg4WL1795bdbucaAeAu2LFjR/Ofv/zyyxa7cxobG1VcXMz1L7itnJwc0wkOj70LbfO/1weVlJRo0qRJyszMVFxcnNzc3EznAXASXB8EAAAAOCG73a5nnnlGO3fu1JAhQxQeHi673a7jx4/r6NGjmjx5srZt22Y6E3B6f/2S7a9P4/4vDw8PBQUFafXq1Zo0aZKJPMBlsHfh9hYsWKDCwkIFBgZq9uzZSkpKanWlGQC0BUMBAAAAwAnl5ORo0aJF2r59u6Kiolqc7dmzRwkJCcrMzNTMmTMNFQKuJTg4WAcPHmTRKXCPsHfh9tzc3BQYGKhhw4b97duAHfnvCEDbcH0QAAAA4IQ2b96sV199tdVAQJLGjh2rxYsXKz8/n6EA8C/98MMPunjxoioqKpqf5eXladmyZbp27ZoSEhL07rvvtvhFJoA7x96F25s5cyZXAwK4K3hTAAAAAHBCvXv31q5duzR06NCbnh8+fFgTJkxQVVVV+4YBLiYuLk5RUVF65ZVXJElHjx5VRESEkpOT9eCDD+rtt9/Wiy++qNdff91sKAAAQBuxgQQAAABwQhcvXpS/v/8tz/39/VVTU9OORYBrOnLkiKKjo5u/Liws1BNPPKFNmzbp5Zdf1rp167RlyxaDhQAAAHeGoQAAAADghBobG9Wp061vA3V3d1dDQ0M7FgGuqaampsUA7ptvvlFcXFzz148//rjOnTtnIg0AAOAfYacAAAAA4ITsdruSk5NveY95XV1dOxcBrsnf318VFRXq37+/6uvrVVZWpuXLlzefX716VR4eHgYLAQAA7gxDAQAAAMAJ3Wwh4/9jyTDw78XFxWnx4sVauXKltm3bpm7dumn06NHN51arVaGhoQYLAQAA7gyLhgEAAAAAuIXq6mpNmTJF3333nby8vPTBBx/oueeeaz6Pjo7Wk08+qRUrVhisBAAAaDuGAgAAAAAA3Mbly5fl5eUld3f3Fs8vXrwoLy8veXp6GioDAAC4MwwFAAAAAAAAAADoINxMBwAAAAAAAAAAgPbBUAAAAAAAAAAAgA6CoQAAAAAAAAAAAB0EQwEAAAAAAAAAADoIhgIAAAAAAAAAAHQQDAUAAAAAAAAAAOggGAoAAAAAAAAAANBBMBQAAAAAAAAAAKCD+A8FSHWgYNasYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 上面列太多了，看相关性系数不好看，我们可以绘制热力图看\n",
    "# 绘制热力图\n",
    "plt.figure(figsize=(20,8))\n",
    "sns.heatmap(cor,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "243430de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除相关性系数大于0.8的列  删除StreamingTV、StreamingMovies中的一行\n",
    "del df_drop['StreamingTV']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "09b471dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender              0\n",
       "SeniorCitizen       0\n",
       "Partner             0\n",
       "Dependents          0\n",
       "MultipleLines       0\n",
       "InternetService     0\n",
       "OnlineSecurity      0\n",
       "OnlineBackup        0\n",
       "DeviceProtection    0\n",
       "TechSupport         0\n",
       "StreamingMovies     0\n",
       "Contract            0\n",
       "PaperlessBilling    0\n",
       "PaymentMethod       0\n",
       "MonthlyCharges      0\n",
       "TotalCharges        0\n",
       "Churn               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drop.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f7286c7",
   "metadata": {},
   "source": [
    "8.拆分训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5d11f27e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 先纵向拆分，把目标列和非目标列拆分\n",
    "X = df_drop.drop(axis=1,columns=['Churn'])  #非目标列\n",
    "y = df_drop['Churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cb4a1b71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 横向拆分成训练集和测试集\n",
    "from sklearn.model_selection import train_test_split\n",
    "# 测试集的拆分比例  random_state随机种子，随便取值，保证每次拆分都是一样的\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c9c8c66",
   "metadata": {},
   "source": [
    "9.使用KNN算法进行建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "84fcfe59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier  # 分类算法  分类器\n",
    "from sklearn.neighbors import KNeighborsRegressor   # 回归算法  回归器\n",
    "\n",
    "# 建模\n",
    "model_knn = KNeighborsClassifier()\n",
    "model_knn.fit(X=X_train,y=y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1768970",
   "metadata": {},
   "source": [
    "10.使用score()方法对模型进行评分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "90e01f97",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\neighbors\\_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7671557027922385"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_knn.score(X=X_test,y=y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ade438d2",
   "metadata": {},
   "source": [
    "11.使用朴素贝叶斯算法进行建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "15768427",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB  #高斯贝叶斯分类器\n",
    "\n",
    "model = GaussianNB()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "76a0870c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7203028868906768"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac18d572",
   "metadata": {},
   "source": [
    "12.使用SVM(支持向量机Support Vector Machine)建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6f1ebec0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC  #分类器\n",
    "from sklearn.svm import SVR  #回归器\n",
    "\n",
    "model = SVC(kernel='rbf')  # 参数kernel:核函数，默认为'rbf'(径向基(高斯)核函数)\n",
    "model.fit(X=X_train,y=y_train) #训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9455d92f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7283483199242783"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X=X_test,y=y_test)  #评分 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5794233",
   "metadata": {},
   "source": [
    "13.使用决策树算法进行建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "189983ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 13.1测试正常默认的决策树分类算法\n",
    "from sklearn.tree import DecisionTreeClassifier #分类器\n",
    "from sklearn.tree import DecisionTreeRegressor #回归器\n",
    "\n",
    "model = DecisionTreeClassifier() #max_depth:树的深度，也就是树最大层数，选择数据几列的意思，如果设置了这个值就叫先剪枝\n",
    "model.fit(X=X_train,y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "92459aae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7283483199242783"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X=X_test,y=y_test)  #评分 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5b9db7f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes', 'No', 'No', ..., 'No', 'Yes', 'No'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X=X_test)  #模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7dbdcf9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=8)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=8)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=8)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 13.2 先剪枝后剪枝的用法\n",
    "model = DecisionTreeClassifier(max_depth=8) #max_depth:树的深度，也就是树最大层数，选择数据几列的意思，如果设置了这个值就叫先剪枝\n",
    "model.fit(X=X_train,y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0d06aba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7823000473260767"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X=X_test,y=y_test)  #评分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "373b676b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes', 'No', 'No', ..., 'No', 'No', 'No'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)  # 模型预测"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38dddeb3",
   "metadata": {},
   "source": [
    "决策树后剪枝"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0740584b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00501072, 0.00262677, 0.00832304, 0.00631992, 0.01357434,\n",
       "       0.06990048, 0.10831351, 0.00819919, 0.0037317 , 0.01000131,\n",
       "       0.00298881, 0.3854188 , 0.01273575, 0.0275034 , 0.12779779,\n",
       "       0.20755445])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_importances_      # 模型各个属性的重要度，就是对模型分数的重要程度，用这个可以看出哪些属性是比较重要的，值越大越重要\n",
    "#如果值非常小，可以考虑删除该列后再做评分，看评分是否有变化，如果没有变化则说明该列确实不重要，这就叫后剪枝"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ac31af95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['gender', 'SeniorCitizen', 'Partner', 'Dependents',\n",
       "       'MultipleLines', 'InternetService', 'OnlineSecurity',\n",
       "       'OnlineBackup', 'DeviceProtection', 'TechSupport',\n",
       "       'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod',\n",
       "       'MonthlyCharges', 'TotalCharges'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_names_in_   #输入到模型的属性，查看模型重要度中各个属性值是哪些，和model.feature_importances是对应的，\n",
    "# 在model.feature_importances输出值时看不出重要度对应的是哪些属性，就可以用这个查看，和model.feature_importances的数组是一一对应的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4c9775e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'Partner', 'Dependents', 'MultipleLines', 'InternetService',\n",
       "       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',\n",
       "       'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges',\n",
       "       'TotalCharges'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 观察model.feature_importances_和model.feature_names_in_ 两个输出的值发现：StreamingMovies的重要度非常低，只有0.00298881，可以考虑删掉\n",
    "# X_train = X_train.drop(axis=1,columns=['StreamingMovies'])   #第一次发现StreamingMovies列重要度比较低删掉\n",
    "# 训练集删了一列，那么测试集也要相应的删除那列 #第一次发现StreamingMovies列重要度比较低删掉\n",
    "# X_test = X_test.drop(axis=1,columns=['StreamingMovies'])\n",
    "\n",
    "#第二次发现SeniorCitizen列也比较低，那么把这列也删了   ,这个过程可以重复观察\n",
    "X_train = X_train.drop(axis=1,columns=['StreamingMovies','SeniorCitizen'])\n",
    "X_test = X_test.drop(axis=1,columns=['StreamingMovies','SeniorCitizen'])\n",
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ba2d3ac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删掉重要度不高的列之后，再进行模型训练，再看评分，然后再看重要度，继续删，递归\n",
    "model = DecisionTreeClassifier()  #又重新建模\n",
    "model.fit(X=X_train,y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ae61a539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7349739706578324"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X=X_test,y=y_test)  #评分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "da4770e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes', 'No', 'No', ..., 'No', 'Yes', 'No'], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)  # 模型预测   预测的时候只有X，没有y，因为y是目标列，本来就是进行预测的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cdce933e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#13.3  显示决策树    #考点，实验时有可能让显示这张图\n",
    "from sklearn import tree\n",
    "plt.figure(figsize=(20,8))\n",
    "tree.plot_tree(model,filled=True)\n",
    "plt.savefig('./tree.png')    #把显示出来的树保存到本地\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c7e311dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 13.4用决策树中的回归算法试试，测试一下在分类场景中用了回归算法会出现什么情况？？\n",
    "# 先纵向拆分，把目标列和非目标列拆分\n",
    "X = df_drop.drop(axis=1,columns=['Churn'])  #非目标列\n",
    "y = pd.factorize(df_drop['Churn'])[0]   #将Churn中的值yes、no替换成了0和1  二元属性做特征编码label-encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3f00430f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y)  #y去重，得到Churn中只有两个值：0、1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2c0c76b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 横向拆分成训练集和测试集\n",
    "from sklearn.model_selection import train_test_split\n",
    "# 测试集的拆分比例  random_state随机种子，随便取值，保证每次拆分都是一样的\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "da77ad57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = DecisionTreeRegressor()\n",
    "model.fit(X=X_train,y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "71ee421a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.5       , 0.66666667, 1.        ])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(model.predict(X_test))  #这里预测的目标列本来只有两个值0,1。但是这里出现了0.5、0.66666667\n",
    "#说明在分类场景中用了回归算法会出现新的值，就会有问题\n",
    "#所以在分类场景中不要用回归器，相反，在回归场景中不要用分类器，该用分类用分类，该用回归用回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8b712c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 0., 0., ..., 0., 1., 0.])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfdf2f57",
   "metadata": {},
   "source": [
    "14.使用随机森林算法进行建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5ac59c50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#重置数据\n",
    "# 先纵向拆分，把目标列和非目标列拆分\n",
    "X = df_drop.drop(axis=1,columns=['Churn'])  #非目标列\n",
    "y = df_drop['Churn']\n",
    "# 横向拆分成训练集和测试集\n",
    "from sklearn.model_selection import train_test_split\n",
    "# 测试集的拆分比例  random_state随机种子，随便取值，保证每次拆分都是一样的\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier   #分类器   ensemble集成的意思\n",
    "from sklearn.ensemble import RandomForestRegressor   #回归器\n",
    "\n",
    "model = RandomForestClassifier()#参数n_estimators：默认是50，表示用多少个决策树，这个参数对模型的效率影响比较大，越大训练的时间越久，可以用于模型调优，让其分数越高\n",
    "model.fit(X=X_train,y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "08a163b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7974443918599148"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X=X_test,y=y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdcadc0a",
   "metadata": {},
   "source": [
    "15.使用Adaboost(梯度算法)算法进行建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d42cf463",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostClassifier()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier   #分类器  自适应提升算法模型精度，有时候不一定比bagging思想分数高，但理论上是高的\n",
    "from sklearn.ensemble import AdaBoostRegressor   #回归器\n",
    "\n",
    "model = AdaBoostClassifier() #参数n_estimators：默认是50，表示用多少个决策树，这个参数对模型的效率影响比较大，越大训练的时间越久，可以用于模型调优，让其分数越高\n",
    "model.fit(X=X_train,y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ac3fc2cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8045433033601515"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X=X_test,y=y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "858008e1",
   "metadata": {},
   "source": [
    "16.使用GBDT(自适应提升算法)算法进行建模   不太适用于分类场景，不是说不能用，只是最终的结果不是那么好，回归场景中的准确率更高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "bef85279",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier  #分类器\n",
    "from sklearn.ensemble import GradientBoostingRegressor  #回归器\n",
    "\n",
    "model = GradientBoostingClassifier() #参数n_estimators：默认是100，表示用多少个决策树，这个参数对模型的效率影响比较大，越大训练的时间越久，可以用于模型调优，让其分数越高\n",
    "model.fit(X=X_train,y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2e2eae7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8064363464268812"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X=X_test,y=y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bae8ddff",
   "metadata": {},
   "source": [
    "17.使用XGBoost算法进行建模 本质上还是一个GBDT，但是在其上做了很多改进，把速度和效率做到了极致"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "28514f2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 引入第三方XGBoost库   XGBoost并没有放在sklearn库中    用清华的镜像安装   XGBoost是一个单独的库\n",
    "# 卧槽，下面pip这一行中不能加注释，有其他的文字就会报错，就是不能出现其他东西"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6439f57f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: xgboost in c:\\users\\lenovo\\appdata\\roaming\\python\\python310\\site-packages (2.0.3)\n",
      "Requirement already satisfied: scipy in d:\\soft\\anaconda\\lib\\site-packages (from xgboost) (1.10.0)\n",
      "Requirement already satisfied: numpy in d:\\soft\\anaconda\\lib\\site-packages (from xgboost) (1.23.5)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -yspark (c:\\users\\lenovo\\appdata\\roaming\\python\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -yspark (c:\\users\\lenovo\\appdata\\roaming\\python\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -yspark (c:\\users\\lenovo\\appdata\\roaming\\python\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -yspark (c:\\users\\lenovo\\appdata\\roaming\\python\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -yspark (c:\\users\\lenovo\\appdata\\roaming\\python\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -yspark (c:\\users\\lenovo\\appdata\\roaming\\python\\python310\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "pip install xgboost -i https://pypi.tuna.tsinghua.edu.cn/simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "290eac7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier #分类器\n",
    "from xgboost import XGBRegressor #回归器\n",
    "\n",
    "# xgboost算法要求目标列的数据必须是从0开始的，yes和no必须换成0和1\n",
    "model = XGBClassifier()\n",
    "model.fit(X=X_train,y=pd.factorize(y_train)[0])   #xgboost的目标列必须是数值类型的，但是目标列现在的值只有yes、no,所以上面重置数据，再重新把目标列处理下\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e7546453",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20681495504022718"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X=X_test,y=pd.factorize(y_test)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5a8af69",
   "metadata": {},
   "source": [
    "18.使用LightGBM算法建模，本质上也是一个GBDT，但是在其上做了很多改进，是微软写的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5eeb3576",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 引入lightbgm库，也是一个单独的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a4a0f114",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: lightgbm in c:\\users\\lenovo\\appdata\\roaming\\python\\python310\\site-packages (4.3.0)\n",
      "Requirement already satisfied: scipy in d:\\soft\\anaconda\\lib\\site-packages (from lightgbm) (1.10.0)\n",
      "Requirement already satisfied: numpy in d:\\soft\\anaconda\\lib\\site-packages (from lightgbm) (1.23.5)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -yspark (c:\\users\\lenovo\\appdata\\roaming\\python\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -yspark (c:\\users\\lenovo\\appdata\\roaming\\python\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -yspark (c:\\users\\lenovo\\appdata\\roaming\\python\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -yspark (c:\\users\\lenovo\\appdata\\roaming\\python\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -yspark (c:\\users\\lenovo\\appdata\\roaming\\python\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -yspark (c:\\users\\lenovo\\appdata\\roaming\\python\\python310\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "pip install lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5a6255bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 1295, number of negative: 3635\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001280 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 548\n",
      "[LightGBM] [Info] Number of data points in the train set: 4930, number of used features: 16\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.262677 -> initscore=-1.032098\n",
      "[LightGBM] [Info] Start training from score -1.032098\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LGBMClassifier()"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lightgbm import LGBMClassifier #分类器\n",
    "from lightgbm import LGBMRegressor #回归器\n",
    "\n",
    "model = LGBMClassifier()\n",
    "model.fit(X=X_train,y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "76269954",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7979176526265973"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X=X_test,y=y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94efb852",
   "metadata": {},
   "source": [
    "19.模型评估-建模完成以后就是模型评估\n",
    "    这里可能会出现一道题：编程在下面\n",
    "        使用f1_score对xgboost算法进行评估(就是对偏差的考察)，之后在使用交叉验证对xgboost算法进行评估(考察方差)，评估其在不同数据集上的表现情况，指定cv为5，cv是交叉验证中指定的n的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f122f6a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier #分类器\n",
    "from xgboost import XGBRegressor #回归器\n",
    "\n",
    "# xgboost算法要求目标列的数据必须是从0开始的，yes和no必须换成0和1\n",
    "model = XGBClassifier()\n",
    "model.fit(X=X_train,y=pd.factorize(y_train)[0])   #xgboost的目标列必须是数值类型的，但是目标列现在的值只有yes、no,所以上面重置数据，再重新把目标列处理下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0c7ccba4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2044584979953023"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 19.1 偏差\n",
    "from sklearn.metrics import f1_score  # f1_score是一个算法，主要是利用偏差对模型的评估\n",
    "\n",
    "# y_true:真实值，在拆分数据的时候拆分了训练集和测试集，测试集就相当于答案，用来和预测值相比\n",
    "# y_pred:预测值\n",
    "y_pred = model.predict(X=X_test)\n",
    "# xgboost算法要求目标列的数据必须是从0开始的，yes和no必须换成0和1，用其他算法就不用把目标列转成数值类型的 \n",
    "f1_score(y_true=pd.factorize(y_test)[0],y_pred=y_pred,average='macro')    #average如果是列是非数值类型的，这个改成'macro'值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f7400c0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 19.2 交叉验证\n",
    "from sklearn.model_selection import cross_val_score  #交叉验证的库  cross_val_score执行交叉验证并返回模型的性能评估结果\n",
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "# 交叉验证需要的东西：模型 数据  评分   拆分为几份\n",
    "# 数据:只用训练集或者全部的数据都行\n",
    "# estimator：模型 score：评分 cv：交叉验证分成几份\n",
    "# X:这里可以用X_train,也可以用X，当数据量比较大的时候，可以用X_train，当数据量比较小的时候，用X，不用横向拆分后的数据X_train\n",
    "# y:这里可以用y_train,也可以用y，当数据量比较大的时候，可以用y_train，当数据量比较小的时候，用y，不用横向拆分后的数据y_train\n",
    "# make_scorer用来对每一份数据进行模型评分的，里面需要的参数为score_func：用什么算法进行评分，这里指定f1_score算法\n",
    "cv_score = cross_val_score(estimator=model,X=X,y=pd.factorize(y)[0],scoring=make_scorer(f1_score,average='macro'),cv=5)\n",
    "# 数据量比较大的时候会比较慢，考试的时候如果不是题目指定建议cv=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "25617ad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.69511968, 0.71937596, 0.69070697, 0.70937059, 0.71819373])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_score"
   ]
  }
 ],
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